1
|
Ferraz-Caetano J, Teixeira F, Cordeiro MNDS. Data-driven, explainable machine learning model for predicting volatile organic compounds' standard vaporization enthalpy. CHEMOSPHERE 2024; 359:142257. [PMID: 38719116 DOI: 10.1016/j.chemosphere.2024.142257] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Revised: 04/18/2024] [Accepted: 05/04/2024] [Indexed: 05/21/2024]
Abstract
The accurate prediction of standard vaporization enthalpy (ΔvapHm°) for volatile organic compounds (VOCs) is of paramount importance in environmental chemistry, industrial applications and regulatory compliance. To overcome traditional experimental methods for predicting ΔvapHm° of VOCs, machine learning (ML) models enable a high-throughput, cost-effective property estimation. But despite a rising momentum, existing ML algorithms still present limitations in prediction accuracy and broad chemical applications. In this work, we present a data driven, explainable supervised ML model to predict ΔvapHm° of VOCs. The model was built on an established experimental database of 2410 unique molecules and 223 VOCs categorized by chemical groups. Using supervised ML regression algorithms, the Random Forest successfully predicted VOCs' ΔvapHm° with a mean absolute error of 3.02 kJ mol-1 and a 95% test score. The model was successfully validated through the prediction of ΔvapHm° for a known database of VOCs and through molecular group hold-out tests. Through chemical feature importance analysis, this explainable model revealed that VOC polarizability, connectivity indexes and electrotopological state are key for the model's prediction accuracy. We thus present a replicable and explainable model, which can be further expanded towards the prediction of other thermodynamic properties of VOCs.
Collapse
Affiliation(s)
- José Ferraz-Caetano
- LAQV-REQUIMTE - Department of Chemistry and Biochemistry - Faculty of Sciences, University of Porto - Rua do Campo Alegre, S/N, 4169-007, Porto, Portugal.
| | - Filipe Teixeira
- CQUM - Centre of Chemistry, University of Minho, Campus de Gualtar, 4710-057, Braga, Portugal
| | - M Natália D S Cordeiro
- LAQV-REQUIMTE - Department of Chemistry and Biochemistry - Faculty of Sciences, University of Porto - Rua do Campo Alegre, S/N, 4169-007, Porto, Portugal.
| |
Collapse
|
2
|
Wei S, Xia X, Bi S, Hu S, Wu X, Hsu HY, Zou X, Huang K, Zhang DW, Sun Q, Bard AJ, Yu ET, Ji L. Metal-insulator-semiconductor photoelectrodes for enhanced photoelectrochemical water splitting. Chem Soc Rev 2024. [PMID: 38833171 DOI: 10.1039/d3cs00820g] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/06/2024]
Abstract
Photoelectrochemical (PEC) water splitting provides a scalable and integrated platform to harness renewable solar energy for green hydrogen production. The practical implementation of PEC systems hinges on addressing three critical challenges: enhancing energy conversion efficiency, ensuring long-term stability, and achieving economic viability. Metal-insulator-semiconductor (MIS) heterojunction photoelectrodes have gained significant attention over the last decade for their ability to efficiently segregate photogenerated carriers and mitigate corrosion-induced semiconductor degradation. This review discusses the structural composition and interfacial intricacies of MIS photoelectrodes tailored for PEC water splitting. The application of MIS heterostructures across various semiconductor light-absorbing layers, including traditional photovoltaic-grade semiconductors, metal oxides, and emerging materials, is presented first. Subsequently, this review elucidates the reaction mechanisms and respective merits of vacuum and non-vacuum deposition techniques in the fabrication of the insulator layers. In the context of the metal layers, this review extends beyond the conventional scope, not only by introducing metal-based cocatalysts, but also by exploring the latest advancements in molecular and single-atom catalysts integrated within MIS photoelectrodes. Furthermore, a systematic summary of carrier transfer mechanisms and interface design principles of MIS photoelectrodes is presented, which are pivotal for optimizing energy band alignment and enhancing solar-to-chemical conversion efficiency within the PEC system. Finally, this review explores innovative derivative configurations of MIS photoelectrodes, including back-illuminated MIS photoelectrodes, inverted MIS photoelectrodes, tandem MIS photoelectrodes, and monolithically integrated wireless MIS photoelectrodes. These novel architectures address the limitations of traditional MIS structures by effectively coupling different functional modules, minimizing optical and ohmic losses, and mitigating recombination losses.
Collapse
Affiliation(s)
- Shice Wei
- School of Microelectronics & Jiashan Fudan Institute, Fudan University, Shanghai 200433, China.
| | - Xuewen Xia
- School of Materials Science and Engineering, Shanghai University, Shanghai 200444, China.
| | - Shuai Bi
- Department of Chemistry, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon, Hong Kong 999077, China
| | - Shen Hu
- School of Microelectronics & Jiashan Fudan Institute, Fudan University, Shanghai 200433, China.
| | - Xuefeng Wu
- School of Microelectronics & Jiashan Fudan Institute, Fudan University, Shanghai 200433, China.
| | - Hsien-Yi Hsu
- Department of Chemistry, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon, Hong Kong 999077, China
| | - Xingli Zou
- School of Materials Science and Engineering, Shanghai University, Shanghai 200444, China.
| | - Kai Huang
- Department of Physics, Xiamen University, Xiamen 361005, China.
| | - David W Zhang
- School of Microelectronics & Jiashan Fudan Institute, Fudan University, Shanghai 200433, China.
| | - Qinqqing Sun
- School of Microelectronics & Jiashan Fudan Institute, Fudan University, Shanghai 200433, China.
| | - Allen J Bard
- Department of Chemistry, The University of Texas at Austin, Texas 78713, USA
| | - Edward T Yu
- Department of Electrical and Computer Engineering, The University of Texas at Austin, Texas 78758, USA.
| | - Li Ji
- School of Microelectronics & Jiashan Fudan Institute, Fudan University, Shanghai 200433, China.
| |
Collapse
|
3
|
Shrestha S, Barvenik KJ, Chen T, Yang H, Li Y, Kesavan MM, Little JM, Whitley HC, Teng Z, Luo Y, Tubaldi E, Chen PY. Machine intelligence accelerated design of conductive MXene aerogels with programmable properties. Nat Commun 2024; 15:4685. [PMID: 38824129 PMCID: PMC11144242 DOI: 10.1038/s41467-024-49011-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2023] [Accepted: 05/14/2024] [Indexed: 06/03/2024] Open
Abstract
Designing ultralight conductive aerogels with tailored electrical and mechanical properties is critical for various applications. Conventional approaches rely on iterative, time-consuming experiments across a vast parameter space. Herein, an integrated workflow is developed to combine collaborative robotics with machine learning to accelerate the design of conductive aerogels with programmable properties. An automated pipetting robot is operated to prepare 264 mixtures of Ti3C2Tx MXene, cellulose, gelatin, and glutaraldehyde at different ratios/loadings. After freeze-drying, the aerogels' structural integrity is evaluated to train a support vector machine classifier. Through 8 active learning cycles with data augmentation, 162 unique conductive aerogels are fabricated/characterized via robotics-automated platforms, enabling the construction of an artificial neural network prediction model. The prediction model conducts two-way design tasks: (1) predicting the aerogels' physicochemical properties from fabrication parameters and (2) automating the inverse design of aerogels for specific property requirements. The combined use of model interpretation and finite element simulations validates a pronounced correlation between aerogel density and compressive strength. The model-suggested aerogels with high conductivity, customized strength, and pressure insensitivity allow for compression-stable Joule heating for wearable thermal management.
Collapse
Affiliation(s)
- Snehi Shrestha
- Department of Chemical and Biomolecular Engineering, University of Maryland, College Park, MD, 20742, USA
| | - Kieran James Barvenik
- Department of Mechanical Engineering, University of Maryland, College Park, MD, 20742, USA
| | - Tianle Chen
- Department of Chemical and Biomolecular Engineering, University of Maryland, College Park, MD, 20742, USA
| | - Haochen Yang
- Department of Chemical and Biomolecular Engineering, University of Maryland, College Park, MD, 20742, USA
| | - Yang Li
- Department of Chemical and Biomolecular Engineering, University of Maryland, College Park, MD, 20742, USA
| | - Meera Muthachi Kesavan
- Department of Chemical and Biomolecular Engineering, University of Maryland, College Park, MD, 20742, USA
| | - Joshua M Little
- Department of Chemical and Biomolecular Engineering, University of Maryland, College Park, MD, 20742, USA
| | - Hayden C Whitley
- Department of Chemical and Biomolecular Engineering, University of Maryland, College Park, MD, 20742, USA
| | - Zi Teng
- US Department of Agriculture, Agricultural Research Service, Food Quality Laboratory and Environment Microbial Food Safety Laboratory, Beltsville Agricultural Research Center, Beltsville, MD, 20725, USA
| | - Yaguang Luo
- US Department of Agriculture, Agricultural Research Service, Food Quality Laboratory and Environment Microbial Food Safety Laboratory, Beltsville Agricultural Research Center, Beltsville, MD, 20725, USA
| | - Eleonora Tubaldi
- Department of Mechanical Engineering, University of Maryland, College Park, MD, 20742, USA.
- Maryland Robotics Center, College Park, MD, 20742, USA.
| | - Po-Yen Chen
- Department of Chemical and Biomolecular Engineering, University of Maryland, College Park, MD, 20742, USA.
- Maryland Robotics Center, College Park, MD, 20742, USA.
| |
Collapse
|
4
|
Yang C, Huang W, Lin Y, Cao S, Wang H, Sun Y, Fang T, Wang M, Kong D. Stretchable MXene/Carbon Nanotube Bilayer Strain Sensors with Tunable Sensitivity and Working Ranges. ACS APPLIED MATERIALS & INTERFACES 2024. [PMID: 38822785 DOI: 10.1021/acsami.4c04770] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2024]
Abstract
Stretchable strain sensors have gained increasing popularity as wearable devices to convert mechanical deformation of the human body into electrical signals. Two-dimensional transition metal carbides (Ti3C2Tx MXene) are promising candidates to achieve excellent sensitivity. However, MXene films have been limited in operating strain ranges due to rapid crack propagation during stretching. In this regard, this study reports MXene/carbon nanotube bilayer films with tunable sensitivity and working ranges. The device is fabricated using a scalable process involving spray deposition of well-dispersed nanomaterial inks. The bilayer sensor's high sensitivity is attributed to the cracks that form in the MXene film, while the compliant carbon nanotube layer extends the working range by maintaining conductive pathways. Moreover, the response of the sensor is easily controlled by tuning the MXene loading, achieving a gauge factor of 9039 within 15% strain at 1.92 mg/cm2 and a gauge factor of 1443 within 108% strain at 0.55 mg/cm2. These tailored properties can precisely match the operation requirements during the wearable application, providing accurate monitoring of various body movements and physiological activities. Additionally, a smart glove with multiple integrated strain sensors is demonstrated as a human-machine interface for the real-time recognition of hand gestures based on a machine-learning algorithm. The design strategy presented here provides a convenient avenue to modulate strain sensors for targeted applications.
Collapse
Affiliation(s)
- Cheng Yang
- College of Engineering and Applied Sciences, and Jiangsu Key Laboratory of Artificial Functional Materials, Nanjing University, Nanjing 210023, China
- State Key Laboratory of Analytical Chemistry for Life Science, Nanjing 210023, China
| | - Weixi Huang
- College of Engineering and Applied Sciences, and Jiangsu Key Laboratory of Artificial Functional Materials, Nanjing University, Nanjing 210023, China
- State Key Laboratory of Analytical Chemistry for Life Science, Nanjing 210023, China
| | - Yong Lin
- College of Engineering and Applied Sciences, and Jiangsu Key Laboratory of Artificial Functional Materials, Nanjing University, Nanjing 210023, China
- State Key Laboratory of Analytical Chemistry for Life Science, Nanjing 210023, China
| | - Shitai Cao
- College of Engineering and Applied Sciences, and Jiangsu Key Laboratory of Artificial Functional Materials, Nanjing University, Nanjing 210023, China
- State Key Laboratory of Analytical Chemistry for Life Science, Nanjing 210023, China
| | - Hao Wang
- College of Engineering and Applied Sciences, and Jiangsu Key Laboratory of Artificial Functional Materials, Nanjing University, Nanjing 210023, China
- State Key Laboratory of Analytical Chemistry for Life Science, Nanjing 210023, China
| | - Yuping Sun
- College of Engineering and Applied Sciences, and Jiangsu Key Laboratory of Artificial Functional Materials, Nanjing University, Nanjing 210023, China
- State Key Laboratory of Analytical Chemistry for Life Science, Nanjing 210023, China
| | - Ting Fang
- College of Engineering and Applied Sciences, and Jiangsu Key Laboratory of Artificial Functional Materials, Nanjing University, Nanjing 210023, China
- State Key Laboratory of Analytical Chemistry for Life Science, Nanjing 210023, China
| | - Menglu Wang
- College of Engineering and Applied Sciences, and Jiangsu Key Laboratory of Artificial Functional Materials, Nanjing University, Nanjing 210023, China
- State Key Laboratory of Analytical Chemistry for Life Science, Nanjing 210023, China
| | - Desheng Kong
- College of Engineering and Applied Sciences, and Jiangsu Key Laboratory of Artificial Functional Materials, Nanjing University, Nanjing 210023, China
- State Key Laboratory of Analytical Chemistry for Life Science, Nanjing 210023, China
| |
Collapse
|
5
|
Tsutsui Y, Yanaka I, Takeda K, Kondo M, Takizawa S, Kojima R, Konishi A, Yasuda M. Selective recognition between aromatics and aliphatics by cage-shaped borates supported by a machine learning approach. Org Biomol Chem 2024; 22:4283-4291. [PMID: 38602393 DOI: 10.1039/d4ob00408f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/12/2024]
Abstract
Selective recognition between hydrocarbon moieties is a longstanding issue. Although we developed a π-pocket Lewis acid catalyst with high selectivity for aromatic aldehydes over aliphatic ones, a general strategy for catalyst design remains elusive. As an approach that transfers the molecular recognition based on multiple cooperative non-covalent interactions within the π-pocket to a rational catalyst design, herein, we demonstrate Lewis acid catalysts showing improved selectivity through the support of an ensemble algorithm with random forest, Ada Boost, and XG Boost as a machine learning (ML) approach. Using 7963 explanatory variables extracted from model hetero-Diels-Alder reactions, the ensemble algorithm predicted the chemoselectivity of unlearned catalysts. Experiments confirmed the prediction. The proposed catalyst shows the highest selective recognition, reminiscing enzymatic catalytic activity. Additionally, a SHapley Additive exPlanations (SHAP) method suggested that the selectivity originates from the polarizability and three-dimensional size of the catalyst. This insight leads to rational design guidelines for Lewis acid catalysts with dispersion forces.
Collapse
Affiliation(s)
- Yuya Tsutsui
- Department of Applied Chemistry, Graduate School of Engineering, Osaka University, Suita, 565-0871, Japan.
| | - Issei Yanaka
- Department of Engineering, Graduate School of Integrated Science and Technology, Shizuoka University, Hamamatsu, 432-8561, Japan.
| | - Kazuhiro Takeda
- Department of Engineering, Graduate School of Integrated Science and Technology, Shizuoka University, Hamamatsu, 432-8561, Japan.
| | - Masaru Kondo
- School of Pharmaceutical Sciences, University of Shizuoka, 52-1 Yada, Suruga-ku, Shizuoka 422-8526, Japan
| | | | - Ryosuke Kojima
- Department of Biomedical Data Intelligence, Graduate School of Medicine, Kyoto University, Sakyo-ku, 606-8507, Japan
| | - Akihito Konishi
- Department of Applied Chemistry, Graduate School of Engineering, Osaka University, Suita, 565-0871, Japan.
- Innovative Catalysis Science Division, Institute for Open and Transdisciplinary Research Initiatives (ICS-OTRI), Osaka University, Suita, 565-0871, Japan
| | - Makoto Yasuda
- Department of Applied Chemistry, Graduate School of Engineering, Osaka University, Suita, 565-0871, Japan.
- Innovative Catalysis Science Division, Institute for Open and Transdisciplinary Research Initiatives (ICS-OTRI), Osaka University, Suita, 565-0871, Japan
| |
Collapse
|
6
|
Zarrouk T, Ibragimova R, Bartók AP, Caro MA. Experiment-Driven Atomistic Materials Modeling: A Case Study Combining X-Ray Photoelectron Spectroscopy and Machine Learning Potentials to Infer the Structure of Oxygen-Rich Amorphous Carbon. J Am Chem Soc 2024; 146:14645-14659. [PMID: 38749497 PMCID: PMC11140750 DOI: 10.1021/jacs.4c01897] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Revised: 05/02/2024] [Accepted: 05/03/2024] [Indexed: 05/30/2024]
Abstract
An important yet challenging aspect of atomistic materials modeling is reconciling experimental and computational results. Conventional approaches involve generating numerous configurations through molecular dynamics or Monte Carlo structure optimization and selecting the one with the closest match to experiment. However, this inefficient process is not guaranteed to succeed. We introduce a general method to combine atomistic machine learning (ML) with experimental observables that produces atomistic structures compatible with experiment by design. We use this approach in combination with grand-canonical Monte Carlo within a modified Hamiltonian formalism, to generate configurations that agree with experimental data and are chemically sound (low in energy). We apply our approach to understand the atomistic structure of oxygenated amorphous carbon (a-COx), an intriguing carbon-based material, to answer the question of how much oxygen can be added to carbon before it fully decomposes into CO and CO2. Utilizing an ML-based X-ray photoelectron spectroscopy (XPS) model trained from GW and density functional theory (DFT) data, in conjunction with an ML interatomic potential, we identify a-COx structures compliant with experimental XPS predictions that are also energetically favorable with respect to DFT. Employing a network analysis, we accurately deconvolve the XPS spectrum into motif contributions, both revealing the inaccuracies inherent to experimental XPS interpretation and granting us atomistic insight into the structure of a-COx. This method generalizes to multiple experimental observables and allows for the elucidation of the atomistic structure of materials directly from experimental data, thereby enabling experiment-driven materials modeling with a degree of realism previously out of reach.
Collapse
Affiliation(s)
- Tigany Zarrouk
- Department
of Chemistry and Materials Science, Aalto
University, Espoo 02150, Finland
| | - Rina Ibragimova
- Department
of Chemistry and Materials Science, Aalto
University, Espoo 02150, Finland
| | - Albert P. Bartók
- Department
of Physics, University of Warwick, Coventry CV4 7AL, U.K.
- Warwick
Centre for Predictive Modelling, School of Engineering, University of Warwick, Coventry CV4 7AL, U.K.
| | - Miguel A. Caro
- Department
of Chemistry and Materials Science, Aalto
University, Espoo 02150, Finland
| |
Collapse
|
7
|
Schilter O, Gutierrez DP, Folkmann LM, Castrogiovanni A, García-Durán A, Zipoli F, Roch LM, Laino T. Combining Bayesian optimization and automation to simultaneously optimize reaction conditions and routes. Chem Sci 2024; 15:7732-7741. [PMID: 38784737 PMCID: PMC11110165 DOI: 10.1039/d3sc05607d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Accepted: 04/05/2024] [Indexed: 05/25/2024] Open
Abstract
Reaching optimal reaction conditions is crucial to achieve high yields, minimal by-products, and environmentally sustainable chemical reactions. With the recent rise of artificial intelligence, there has been a shift from traditional Edisonian trial-and-error optimization to data-driven and automated approaches, which offer significant advantages. Here, we showcase the capabilities of an integrated platform; we conducted simultaneous optimizations of four different terminal alkynes and two reaction routes using an automation platform combined with a Bayesian optimization platform. Remarkably, we achieved a conversion rate of over 80% for all four substrates in 23 experiments, covering ca. 0.2% of the combinatorial space. Further analysis allowed us to identify the influence of different reaction parameters on the reaction outcomes, demonstrating the potential for expedited reaction condition optimization and the prospect of more efficient chemical processes in the future.
Collapse
Affiliation(s)
- Oliver Schilter
- IBM Research Europe Säumerstrasse 4 8803 Rüschlikon Switzerland
- National Center for Competence in Research-Catalysis (NCCR-Catalysis) Switzerland
| | | | - Linnea M Folkmann
- Atinary Technologies Route de la Corniche 4 1066 Epalinges Switzerland
| | | | | | - Federico Zipoli
- IBM Research Europe Säumerstrasse 4 8803 Rüschlikon Switzerland
- National Center for Competence in Research-Catalysis (NCCR-Catalysis) Switzerland
| | - Loïc M Roch
- Atinary Technologies Route de la Corniche 4 1066 Epalinges Switzerland
| | - Teodoro Laino
- IBM Research Europe Säumerstrasse 4 8803 Rüschlikon Switzerland
- National Center for Competence in Research-Catalysis (NCCR-Catalysis) Switzerland
| |
Collapse
|
8
|
Yu H, Díaz A, Lu X, Sun B, Ding Y, Koyama M, He J, Zhou X, Oudriss A, Feaugas X, Zhang Z. Hydrogen Embrittlement as a Conspicuous Material Challenge─Comprehensive Review and Future Directions. Chem Rev 2024; 124:6271-6392. [PMID: 38773953 PMCID: PMC11117190 DOI: 10.1021/acs.chemrev.3c00624] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/24/2024]
Abstract
Hydrogen is considered a clean and efficient energy carrier crucial for shaping the net-zero future. Large-scale production, transportation, storage, and use of green hydrogen are expected to be undertaken in the coming decades. As the smallest element in the universe, however, hydrogen can adsorb on, diffuse into, and interact with many metallic materials, degrading their mechanical properties. This multifaceted phenomenon is generically categorized as hydrogen embrittlement (HE). HE is one of the most complex material problems that arises as an outcome of the intricate interplay across specific spatial and temporal scales between the mechanical driving force and the material resistance fingerprinted by the microstructures and subsequently weakened by the presence of hydrogen. Based on recent developments in the field as well as our collective understanding, this Review is devoted to treating HE as a whole and providing a constructive and systematic discussion on hydrogen entry, diffusion, trapping, hydrogen-microstructure interaction mechanisms, and consequences of HE in steels, nickel alloys, and aluminum alloys used for energy transport and storage. HE in emerging material systems, such as high entropy alloys and additively manufactured materials, is also discussed. Priority has been particularly given to these less understood aspects. Combining perspectives of materials chemistry, materials science, mechanics, and artificial intelligence, this Review aspires to present a comprehensive and impartial viewpoint on the existing knowledge and conclude with our forecasts of various paths forward meant to fuel the exploration of future research regarding hydrogen-induced material challenges.
Collapse
Affiliation(s)
- Haiyang Yu
- Division
of Applied Mechanics, Department of Materials Science and Engineering, Uppsala University, SE-75121 Uppsala, Sweden
| | - Andrés Díaz
- Department
of Civil Engineering, Universidad de Burgos,
Escuela Politécnica Superior, 09006 Burgos, Spain
| | - Xu Lu
- Department
of Mechanical and Industrial Engineering, Norwegian University of Science and Technology (NTNU), 7491 Trondheim, Norway
| | - Binhan Sun
- School of
Mechanical and Power Engineering, East China
University of Science and Technology, Shanghai 200237, China
| | - Yu Ding
- Department
of Structural Engineering, Norwegian University
of Science and Technology (NTNU), Trondheim 7491, Norway
| | - Motomichi Koyama
- Institute
for Materials Research, Tohoku University, Sendai 980-8577, Japan
| | - Jianying He
- Department
of Structural Engineering, Norwegian University
of Science and Technology (NTNU), Trondheim 7491, Norway
| | - Xiao Zhou
- State Key
Laboratory of Metal Matrix Composites, School of Materials Science
and Engineering, Shanghai Jiao Tong University, 200240 Shanghai, China
| | - Abdelali Oudriss
- Laboratoire
des Sciences de l’Ingénieur pour l’Environnement, La Rochelle University, CNRS UMR 7356, 17042 La Rochelle, France
| | - Xavier Feaugas
- Laboratoire
des Sciences de l’Ingénieur pour l’Environnement, La Rochelle University, CNRS UMR 7356, 17042 La Rochelle, France
| | - Zhiliang Zhang
- Department
of Structural Engineering, Norwegian University
of Science and Technology (NTNU), Trondheim 7491, Norway
| |
Collapse
|
9
|
Karadaghi L, Williamson EM, To AT, Forsberg AP, Crans KD, Perkins CL, Hayden SC, LiBretto NJ, Baddour FG, Ruddy DA, Malmstadt N, Habas SE, Brutchey RL. Multivariate Bayesian Optimization of CoO Nanoparticles for CO 2 Hydrogenation Catalysis. J Am Chem Soc 2024; 146:14246-14259. [PMID: 38728108 PMCID: PMC11117399 DOI: 10.1021/jacs.4c03789] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2024] [Revised: 04/30/2024] [Accepted: 05/03/2024] [Indexed: 05/12/2024]
Abstract
The hydrogenation of CO2 holds promise for transforming the production of renewable fuels and chemicals. However, the challenge lies in developing robust and selective catalysts for this process. Transition metal oxide catalysts, particularly cobalt oxide, have shown potential for CO2 hydrogenation, with performance heavily reliant on crystal phase and morphology. Achieving precise control over these catalyst attributes through colloidal nanoparticle synthesis could pave the way for catalyst and process advancement. Yet, navigating the complexities of colloidal nanoparticle syntheses, governed by numerous input variables, poses a significant challenge in systematically controlling resultant catalyst features. We present a multivariate Bayesian optimization, coupled with a data-driven classifier, to map the synthetic design space for colloidal CoO nanoparticles and simultaneously optimize them for multiple catalytically relevant features within a target crystalline phase. The optimized experimental conditions yielded small, phase-pure rock salt CoO nanoparticles of uniform size and shape. These optimized nanoparticles were then supported on SiO2 and assessed for thermocatalytic CO2 hydrogenation against larger, polydisperse CoO nanoparticles on SiO2 and a conventionally prepared catalyst. The optimized CoO/SiO2 catalyst consistently exhibited higher activity and CH4 selectivity (ca. 98%) across various pretreatment reduction temperatures as compared to the other catalysts. This remarkable performance was attributed to particle stability and consistent H* surface coverage, even after undergoing the highest temperature reduction, achieving a more stable catalytic species that resists sintering and carbon occlusion.
Collapse
Affiliation(s)
- Lanja
R. Karadaghi
- Department
of Chemistry, University of Southern California, Los Angeles, California 90089, United States
| | - Emily M. Williamson
- Department
of Chemistry, University of Southern California, Los Angeles, California 90089, United States
| | - Anh T. To
- Catalytic
Carbon Transformation and Scale-Up Center, National Renewable Energy Laboratory, Golden, Colorado 80401, United States
| | - Allison P. Forsberg
- Department
of Chemistry, University of Southern California, Los Angeles, California 90089, United States
| | - Kyle D. Crans
- Department
of Chemistry, University of Southern California, Los Angeles, California 90089, United States
| | - Craig L. Perkins
- Materials
Science Center, National Renewable Energy
Laboratory, Golden, Colorado 80401, United States
| | - Steven C. Hayden
- Materials
Science Center, National Renewable Energy
Laboratory, Golden, Colorado 80401, United States
| | - Nicole J. LiBretto
- Catalytic
Carbon Transformation and Scale-Up Center, National Renewable Energy Laboratory, Golden, Colorado 80401, United States
| | - Frederick G. Baddour
- Catalytic
Carbon Transformation and Scale-Up Center, National Renewable Energy Laboratory, Golden, Colorado 80401, United States
| | - Daniel A. Ruddy
- Catalytic
Carbon Transformation and Scale-Up Center, National Renewable Energy Laboratory, Golden, Colorado 80401, United States
| | - Noah Malmstadt
- Department
of Chemistry, University of Southern California, Los Angeles, California 90089, United States
- Mork
Family Department of Chemical Engineering and Materials Science, University of Southern California, Los Angeles, California 90089, United States
- Department
of Biomedical Engineering, University of
Southern California, Los Angeles, California 90089, United States
- USC Norris
Comprehensive Cancer Center, University
of Southern California, 1441 Eastlake Avenue, Los Angeles, California 90033, United States
| | - Susan E. Habas
- Catalytic
Carbon Transformation and Scale-Up Center, National Renewable Energy Laboratory, Golden, Colorado 80401, United States
| | - Richard L. Brutchey
- Department
of Chemistry, University of Southern California, Los Angeles, California 90089, United States
| |
Collapse
|
10
|
Padhy SP, Chaudhary V, Lim YF, Zhu R, Thway M, Hippalgaonkar K, Ramanujan RV. Experimentally validated inverse design of multi-property Fe-Co-Ni alloys. iScience 2024; 27:109723. [PMID: 38706846 PMCID: PMC11068641 DOI: 10.1016/j.isci.2024.109723] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Revised: 03/11/2024] [Accepted: 04/08/2024] [Indexed: 05/07/2024] Open
Abstract
This study presents a machine learning (ML) framework aimed at accelerating the discovery of multi-property optimized Fe-Ni-Co alloys, addressing the time-consuming, expensive, and inefficient nature of traditional methods of material discovery, development, and deployment. We compiled a detailed heterogeneous database of the magnetic, electrical, and mechanical properties of Fe-Co-Ni alloys, employing a novel ML-based imputation strategy to address gaps in property data. Leveraging this comprehensive database, we developed predictive ML models using tree-based and neural network approaches for optimizing multiple properties simultaneously. An inverse design strategy, utilizing multi-objective Bayesian optimization (MOBO), enabled the identification of promising alloy compositions. This approach was experimentally validated using high-throughput methodology, highlighting alloys such as Fe66.8Co28Ni5.2 and Fe61.9Co22.8Ni15.3, which demonstrated superior properties. The predicted properties data closely matched experimental data within 14% accuracy. Our approach can be extended to a broad range of materials systems to predict novel materials with an optimized set of properties.
Collapse
Affiliation(s)
- Shakti P. Padhy
- School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Republic of Singapore
- Department of Materials Science and Engineering, University of Tennessee, Knoxville, TN 37996, USA
| | - Varun Chaudhary
- Industrial and Materials Science, Chalmers University of Technology, SE-41296 Gothenburg, Sweden
| | - Yee-Fun Lim
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A∗STAR), 2 Fusionopolis Way, Singapore 138634, Republic of Singapore
- Institute of Sustainability for Chemicals, Energy and Environment (ISCE2), Agency for Science Technology and Research (A∗STAR), 1 Pesek Road, Jurong Island, Singapore 627833, Republic of Singapore
| | - Ruiming Zhu
- School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Republic of Singapore
| | - Muang Thway
- School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Republic of Singapore
| | - Kedar Hippalgaonkar
- School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Republic of Singapore
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A∗STAR), 2 Fusionopolis Way, Singapore 138634, Republic of Singapore
| | - Raju V. Ramanujan
- School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Republic of Singapore
| |
Collapse
|
11
|
Yan Z, Wei D, Li X, Chung LW. Accelerating reliable multiscale quantum refinement of protein-drug systems enabled by machine learning. Nat Commun 2024; 15:4181. [PMID: 38755151 PMCID: PMC11099068 DOI: 10.1038/s41467-024-48453-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Accepted: 04/24/2024] [Indexed: 05/18/2024] Open
Abstract
Biomacromolecule structures are essential for drug development and biocatalysis. Quantum refinement (QR) methods, which employ reliable quantum mechanics (QM) methods in crystallographic refinement, showed promise in improving the structural quality or even correcting the structure of biomacromolecules. However, vast computational costs and complex quantum mechanics/molecular mechanics (QM/MM) setups limit QR applications. Here we incorporate robust machine learning potentials (MLPs) in multiscale ONIOM(QM:MM) schemes to describe the core parts (e.g., drugs/inhibitors), replacing the expensive QM method. Additionally, two levels of MLPs are combined for the first time to overcome MLP limitations. Our unique MLPs+ONIOM-based QR methods achieve QM-level accuracy with significantly higher efficiency. Furthermore, our refinements provide computational evidence for the existence of bonded and nonbonded forms of the Food and Drug Administration (FDA)-approved drug nirmatrelvir in one SARS-CoV-2 main protease structure. This study highlights that powerful MLPs accelerate QRs for reliable protein-drug complexes, promote broader QR applications and provide more atomistic insights into drug development.
Collapse
Affiliation(s)
- Zeyin Yan
- Shenzhen Grubbs Institute, Department of Chemistry and Guangdong Provincial Key Laboratory of Catalysis, Southern University of Science and Technology, Shenzhen, 518055, China
| | - Dacong Wei
- Shenzhen Grubbs Institute, Department of Chemistry and Guangdong Provincial Key Laboratory of Catalysis, Southern University of Science and Technology, Shenzhen, 518055, China
| | - Xin Li
- Shenzhen Grubbs Institute, Department of Chemistry and Guangdong Provincial Key Laboratory of Catalysis, Southern University of Science and Technology, Shenzhen, 518055, China
| | - Lung Wa Chung
- Shenzhen Grubbs Institute, Department of Chemistry and Guangdong Provincial Key Laboratory of Catalysis, Southern University of Science and Technology, Shenzhen, 518055, China.
| |
Collapse
|
12
|
Hu S, Ding J, Dong Y, Zhang T, Tang H, Li H. Rapid quantitative analysis of petroleum coke properties by laser-induced breakdown spectroscopy combined with random forest based on a variable selection strategy. RSC Adv 2024; 14:16358-16367. [PMID: 38774617 PMCID: PMC11106651 DOI: 10.1039/d4ra02873b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2024] [Accepted: 05/09/2024] [Indexed: 05/24/2024] Open
Abstract
Driven by the "double carbon" strategy, petroleum coke short-term demand is growing rapidly as a negative electrode material for artificial graphite. The analysis of petroleum coke physicochemical properties has always been an important part of its research, encompassing significant indicators such as ash content, volatile matter and calorific value. A strategy based on laser-induced breakdown spectroscopy (LIBS) in combination with chemometrics is proposed to realize the rapid and accurate quantification of the above properties. LIBS spectra of 46 petroleum coke samples were collected, and an original random forest (RF) calibration model was constructed by optimizing the pretreatment parameters. The RF calibration model was further optimized based on variable importance measures (VIM) and variable importance in projection (VIP) methods. After variable selection, the elemental spectral lines related to ash content, volatile matter and calorific value modeling were screened out, thus initially exploring the correlation between these properties and elements. Under the optimized spectral pretreatment method, VI threshold and model parameters, the mean relative error (MREP) of the prediction set of ash content, volatile matter and calorific value were 0.0881, 0.0527 and 0.006, the root mean square error (RMSEP) of the prediction set of ash content, volatile matter and calorific value were 0.0471%, 0.6178% and 0.2697 MJ kg-1, respectively, and the determination coefficient (RP2) of the prediction set was 0.9187, 0.9820 and 0.9510, respectively. The combination of LIBS technology and chemometric methods can provide powerful technical means for the analysis and evaluation of the physicochemical properties of petroleum coke.
Collapse
Affiliation(s)
- Shunfan Hu
- Key Laboratory of Synthetic and Natural Functional Molecular Chemistry of Ministry of Education, College of Chemistry & Material Science, Northwest University Xi'an 710127 China
| | - Jianming Ding
- Key Laboratory of Synthetic and Natural Functional Molecular Chemistry of Ministry of Education, College of Chemistry & Material Science, Northwest University Xi'an 710127 China
| | - Yan Dong
- China Certification & Inspection Group Shan Dong Co; Ltd Qing Dao 266000 China
| | - Tianlong Zhang
- Key Laboratory of Synthetic and Natural Functional Molecular Chemistry of Ministry of Education, College of Chemistry & Material Science, Northwest University Xi'an 710127 China
| | - Hongsheng Tang
- Key Laboratory of Synthetic and Natural Functional Molecular Chemistry of Ministry of Education, College of Chemistry & Material Science, Northwest University Xi'an 710127 China
| | - Hua Li
- Key Laboratory of Synthetic and Natural Functional Molecular Chemistry of Ministry of Education, College of Chemistry & Material Science, Northwest University Xi'an 710127 China
- College of Chemistry and Chemical Engineering, Xi'an Shiyou University Xi'an 710065 China
| |
Collapse
|
13
|
Guan K, Xu F, Huang X, Li Y, Guo S, Situ Y, Chen Y, Hu J, Liu Z, Liang H, Zhu X, Wu Y, Qiao Z. Deep learning and big data mining for Metal-Organic frameworks with high performance for simultaneous desulfurization and carbon capture. J Colloid Interface Sci 2024; 662:941-952. [PMID: 38382377 DOI: 10.1016/j.jcis.2024.02.098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2023] [Revised: 01/23/2024] [Accepted: 02/12/2024] [Indexed: 02/23/2024]
Abstract
Carbon capture and desulfurization of flue gases are crucial for the achievement of carbon neutrality and sustainable development. In this work, the "one-step" adsorption technology with high-performance metal-organic frameworks (MOFs) was proposed to simultaneously capture the SO2 and CO2. Four machine learning algorithms were used to predict the performance indicators (NCO2+SO2, SCO2+SO2/N2, and TSN) of MOFs, with Multi-Layer Perceptron Regression (MLPR) showing better performance (R2 = 0.93). To address sparse data of MOF chemical descriptors, we introduced the Deep Factorization Machines (DeepFM) model, outperforming MLPR with a higher R2 of 0.95. Then, sensitivity analysis was employed to find that the adsorption heat and porosity were the key factors for SO2 and CO2 capture performance of MOF, while the influence of open alkali metal sites also stood out. Furthermore, we established a kinetic model to batch simulate the breakthrough curves of TOP 1000 MOFs to investigate their dynamic adsorption separation performance for SO2/CO2/N2. The TOP 20 MOFs screened by the dynamic performance highly overlap with those screened by the static performance, with 76 % containing open alkali metal sites. This integrated approach of computational screening, machine learning, and dynamic analysis significantly advances the development of efficient MOF adsorbents for flue gas treatment.
Collapse
Affiliation(s)
- Kexin Guan
- Guangzhou Key Laboratory for New Energy and Green Catalysis, School of Chemistry and Chemical Engineering, Guangzhou University, Guangzhou 510006, China
| | - Fangyi Xu
- Guangzhou Key Laboratory for New Energy and Green Catalysis, School of Chemistry and Chemical Engineering, Guangzhou University, Guangzhou 510006, China
| | - Xiaoshan Huang
- Guangzhou Key Laboratory for New Energy and Green Catalysis, School of Chemistry and Chemical Engineering, Guangzhou University, Guangzhou 510006, China
| | - Yu Li
- Guangzhou Key Laboratory for New Energy and Green Catalysis, School of Chemistry and Chemical Engineering, Guangzhou University, Guangzhou 510006, China
| | - Shuya Guo
- Guangzhou Key Laboratory for New Energy and Green Catalysis, School of Chemistry and Chemical Engineering, Guangzhou University, Guangzhou 510006, China
| | - Yizhen Situ
- Guangzhou Key Laboratory for New Energy and Green Catalysis, School of Chemistry and Chemical Engineering, Guangzhou University, Guangzhou 510006, China; State Key Laboratory of Organic-Inorganic Composites, Beijing University of Chemical Technology, Beijing 100029, China
| | - You Chen
- Guangzhou Key Laboratory for New Energy and Green Catalysis, School of Chemistry and Chemical Engineering, Guangzhou University, Guangzhou 510006, China
| | - Jianming Hu
- College of Economics and Statistics, Guangzhou University, Guangzhou 510006, China
| | - Zili Liu
- Guangzhou Key Laboratory for New Energy and Green Catalysis, School of Chemistry and Chemical Engineering, Guangzhou University, Guangzhou 510006, China
| | - Hong Liang
- Guangzhou Key Laboratory for New Energy and Green Catalysis, School of Chemistry and Chemical Engineering, Guangzhou University, Guangzhou 510006, China
| | - Xin Zhu
- Guangzhou Key Laboratory for New Energy and Green Catalysis, School of Chemistry and Chemical Engineering, Guangzhou University, Guangzhou 510006, China; College of Economics and Statistics, Guangzhou University, Guangzhou 510006, China.
| | - Yufang Wu
- Guangzhou Key Laboratory for New Energy and Green Catalysis, School of Chemistry and Chemical Engineering, Guangzhou University, Guangzhou 510006, China.
| | - Zhiwei Qiao
- Guangzhou Key Laboratory for New Energy and Green Catalysis, School of Chemistry and Chemical Engineering, Guangzhou University, Guangzhou 510006, China.
| |
Collapse
|
14
|
Wang Y, He L, Wang M, Yuan J, Wu S, Li X, Lin T, Huang Z, Li A, Yang Y, Liu X, He Y. The drug loading capacity prediction and cytotoxicity analysis of metal-organic frameworks using stacking algorithms of machine learning. Int J Pharm 2024; 656:124128. [PMID: 38621612 DOI: 10.1016/j.ijpharm.2024.124128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Revised: 03/24/2024] [Accepted: 04/13/2024] [Indexed: 04/17/2024]
Abstract
Metal-organic frameworks (MOFs) have shown excellent performance in the field of drug delivery. Despite the synthesis of a vast array of MOFs exceeding 100,000 varieties, certain formulations have exhibited suboptimal performance characteristics. Therefore, there is a pressing need to enhance their efficacy by identifying MOFs with superior drug loading capacities and minimal cytotoxicity, which can be achieved through machine learning (ML). In this study, a stacking regression model was developed to predict drug loading capacity and cytotoxicity of MOFs using datasets compiled from various literature sources. The model exhibited exceptional predictive capabilities, achieving R2 values of 0.907 for drug loading capacity and 0.856 for cytotoxicity. Furthermore, various model interpretation methods including partial dependence plots, individual conditional expectation, Shapley additive explanation, decision tree, random forest, CatBoost Regressor, and light gradient-boosting machine were employed for feature importance analysis. The results revealed that specific metal atoms such as Zn, Cr, Fe, Zr, and Cu significantly influenced the drug loading capacity and cytotoxicity of MOFs. Through model validation encompassing experimental validation and computational verification, the reliability of the model was thoroughly established. In general, it is a good practice to use ML methods for predicting drug loading capacity and cytotoxicity analysis of MOFs, guiding the development of future property prediction methods for MOFs.
Collapse
Affiliation(s)
- Yang Wang
- School of Biomedical and Pharmaceutical Sciences, Guangdong University of Technology, 100 Waihuanxi Road, Guangzhou Higher Education Mega Center, Guangzhou 510006, PR China
| | - Liqiang He
- School of Biomedical and Pharmaceutical Sciences, Guangdong University of Technology, 100 Waihuanxi Road, Guangzhou Higher Education Mega Center, Guangzhou 510006, PR China
| | - Meijing Wang
- School of Biomedical and Pharmaceutical Sciences, Guangdong University of Technology, 100 Waihuanxi Road, Guangzhou Higher Education Mega Center, Guangzhou 510006, PR China
| | - Jiongpeng Yuan
- School of Biomedical and Pharmaceutical Sciences, Guangdong University of Technology, 100 Waihuanxi Road, Guangzhou Higher Education Mega Center, Guangzhou 510006, PR China
| | - Siwei Wu
- School of Biomedical and Pharmaceutical Sciences, Guangdong University of Technology, 100 Waihuanxi Road, Guangzhou Higher Education Mega Center, Guangzhou 510006, PR China
| | - Xiaojing Li
- School of Biomedical and Pharmaceutical Sciences, Guangdong University of Technology, 100 Waihuanxi Road, Guangzhou Higher Education Mega Center, Guangzhou 510006, PR China
| | - Tong Lin
- School of Biomedical and Pharmaceutical Sciences, Guangdong University of Technology, 100 Waihuanxi Road, Guangzhou Higher Education Mega Center, Guangzhou 510006, PR China
| | - Zihui Huang
- School of Biomedical and Pharmaceutical Sciences, Guangdong University of Technology, 100 Waihuanxi Road, Guangzhou Higher Education Mega Center, Guangzhou 510006, PR China
| | - Andi Li
- School of Biomedical and Pharmaceutical Sciences, Guangdong University of Technology, 100 Waihuanxi Road, Guangzhou Higher Education Mega Center, Guangzhou 510006, PR China
| | - Yuhang Yang
- School of Biomedical and Pharmaceutical Sciences, Guangdong University of Technology, 100 Waihuanxi Road, Guangzhou Higher Education Mega Center, Guangzhou 510006, PR China
| | - Xujie Liu
- School of Biomedical and Pharmaceutical Sciences, Guangdong University of Technology, 100 Waihuanxi Road, Guangzhou Higher Education Mega Center, Guangzhou 510006, PR China.
| | - Yan He
- School of Biomedical and Pharmaceutical Sciences, Guangdong University of Technology, 100 Waihuanxi Road, Guangzhou Higher Education Mega Center, Guangzhou 510006, PR China.
| |
Collapse
|
15
|
Xing B, Rupert TJ, Pan X, Cao P. Neural network kinetics for exploring diffusion multiplicity and chemical ordering in compositionally complex materials. Nat Commun 2024; 15:3879. [PMID: 38724515 PMCID: PMC11082203 DOI: 10.1038/s41467-024-47927-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Accepted: 04/12/2024] [Indexed: 05/12/2024] Open
Abstract
Diffusion involving atom transport from one location to another governs many important processes and behaviors such as precipitation and phase nucleation. The inherent chemical complexity in compositionally complex materials poses challenges for modeling atomic diffusion and the resulting formation of chemically ordered structures. Here, we introduce a neural network kinetics (NNK) scheme that predicts and simulates diffusion-induced chemical and structural evolution in complex concentrated chemical environments. The framework is grounded on efficient on-lattice structure and chemistry representation combined with artificial neural networks, enabling precise prediction of all path-dependent migration barriers and individual atom jumps. To demonstrate the method, we study the temperature-dependent local chemical ordering in a refractory NbMoTa alloy and reveal a critical temperature at which the B2 order reaches a maximum. The atomic jump randomness map exhibits the highest diffusion heterogeneity (multiplicity) in the vicinity of this characteristic temperature, which is closely related to chemical ordering and B2 structure formation. The scalable NNK framework provides a promising new avenue to exploring diffusion-related properties in the vast compositional space within which extraordinary properties are hidden.
Collapse
Affiliation(s)
- Bin Xing
- Center for Complex and Active Materials, University of California, Irvine, CA, USA
- Department of Material Science and Engineering, University of California, Irvine, CA, USA
| | - Timothy J Rupert
- Center for Complex and Active Materials, University of California, Irvine, CA, USA
- Department of Material Science and Engineering, University of California, Irvine, CA, USA
| | - Xiaoqing Pan
- Center for Complex and Active Materials, University of California, Irvine, CA, USA
- Department of Material Science and Engineering, University of California, Irvine, CA, USA
| | - Penghui Cao
- Center for Complex and Active Materials, University of California, Irvine, CA, USA.
- Department of Material Science and Engineering, University of California, Irvine, CA, USA.
- Department of Mechanical and Aerospace Engineering, University of California, Irvine, CA, USA.
| |
Collapse
|
16
|
Zhao XG, Yang Q, Xu Y, Liu QY, Li ZY, Liu XX, Zhao YX, He SG. Machine Learning for Experimental Reactivity of a Set of Metal Clusters toward C-H Activation. J Am Chem Soc 2024; 146:12485-12495. [PMID: 38651836 DOI: 10.1021/jacs.4c00501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/25/2024]
Abstract
Understanding the mechanisms of C-H activation of alkanes is a very important research topic. The reactions of metal clusters with alkanes have been extensively studied to reveal the electronic features governing C-H activation, while the experimental cluster reactivity was qualitatively interpreted case by case in the literature. Herein, we prepared and mass-selected over 100 rhodium-based clusters (RhxVyOz- and RhxCoyOz-) to react with light alkanes, enabling the determination of reaction rate constants spanning six orders of magnitude. A satisfactory model being able to quantitatively describe the rate data in terms of multiple cluster electronic features (average electron occupancy of valence s orbitals, the minimum natural charge on the metal atom, cluster polarizability, and energy gap involved in the agostic interaction) has been constructed through a machine learning approach. This study demonstrates that the general mechanisms governing the very important process of C-H activation by diverse metal centers can be discovered by interpreting experimental data with artificial intelligence.
Collapse
Affiliation(s)
- Xi-Guan Zhao
- State Key Laboratory for Structural Chemistry of Unstable and Stable Species, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, People's Republic of China
- University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
- Beijing National Laboratory for Molecular Sciences and CAS Research/Education Centre of Excellence in Molecular Sciences, Beijing 100190, People's Republic of China
| | - Qi Yang
- State Key Laboratory for Structural Chemistry of Unstable and Stable Species, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, People's Republic of China
- University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
- Beijing National Laboratory for Molecular Sciences and CAS Research/Education Centre of Excellence in Molecular Sciences, Beijing 100190, People's Republic of China
| | - Ying Xu
- State Key Laboratory for Structural Chemistry of Unstable and Stable Species, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, People's Republic of China
- University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
- Beijing National Laboratory for Molecular Sciences and CAS Research/Education Centre of Excellence in Molecular Sciences, Beijing 100190, People's Republic of China
| | - Qing-Yu Liu
- State Key Laboratory for Structural Chemistry of Unstable and Stable Species, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, People's Republic of China
- Beijing National Laboratory for Molecular Sciences and CAS Research/Education Centre of Excellence in Molecular Sciences, Beijing 100190, People's Republic of China
| | - Zi-Yu Li
- State Key Laboratory for Structural Chemistry of Unstable and Stable Species, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, People's Republic of China
- Beijing National Laboratory for Molecular Sciences and CAS Research/Education Centre of Excellence in Molecular Sciences, Beijing 100190, People's Republic of China
| | - Xiao-Xiao Liu
- State Key Laboratory for Structural Chemistry of Unstable and Stable Species, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, People's Republic of China
- University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
- Beijing National Laboratory for Molecular Sciences and CAS Research/Education Centre of Excellence in Molecular Sciences, Beijing 100190, People's Republic of China
| | - Yan-Xia Zhao
- State Key Laboratory for Structural Chemistry of Unstable and Stable Species, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, People's Republic of China
- Beijing National Laboratory for Molecular Sciences and CAS Research/Education Centre of Excellence in Molecular Sciences, Beijing 100190, People's Republic of China
| | - Sheng-Gui He
- State Key Laboratory for Structural Chemistry of Unstable and Stable Species, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, People's Republic of China
- University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
- Beijing National Laboratory for Molecular Sciences and CAS Research/Education Centre of Excellence in Molecular Sciences, Beijing 100190, People's Republic of China
| |
Collapse
|
17
|
Tian Y, Yang X, Chen N, Li C, Yang W. Data-driven interpretable analysis for polysaccharide yield prediction. ENVIRONMENTAL SCIENCE AND ECOTECHNOLOGY 2024; 19:100321. [PMID: 38021368 PMCID: PMC10661693 DOI: 10.1016/j.ese.2023.100321] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Revised: 09/17/2023] [Accepted: 09/17/2023] [Indexed: 12/01/2023]
Abstract
Cornstalks show promise as a raw material for polysaccharide production through xylanase. Rapid and accurate prediction of polysaccharide yield can facilitate process optimization, eliminating the need for extensive experimentation in actual production to refine reaction conditions, thereby saving time and costs. However, the intricate interplay of enzymatic factors poses challenges in predicting and optimizing polysaccharide yield accurately. Here, we introduce an innovative data-driven approach leveraging multiple artificial intelligence techniques to enhance polysaccharide production. We propose a machine learning framework to identify highly accurate polysaccharide yield prediction modeling methods and uncover optimal enzymatic parameter combinations. Notably, Random Forest (RF) and eXtreme Gradient Boost (XGB) demonstrate robust performance, achieving prediction accuracies of 93.0% and 95.6%, respectively, while an independently developed deep neural network (DNN) model achieves 91.1% accuracy. A feature importance analysis of XGB reveals the enzyme solution volume's dominant role (43.7%), followed by time (20.7%), substrate concentration (15%), temperature (15%), and pH (5.6%). Further interpretability analysis unveils complex parameter interactions and potential optimization strategies. This data-driven approach, incorporating machine learning, deep learning, and interpretable analysis, offers a viable pathway for polysaccharide yield prediction and the potential recovery of various agricultural residues.
Collapse
Affiliation(s)
- Yushi Tian
- School of Resource and Environment, Northeast Agriculture University, Harbin, 150030, PR China
| | - Xu Yang
- School of Resource and Environment, Northeast Agriculture University, Harbin, 150030, PR China
| | - Nianhua Chen
- School of Resource and Environment, Northeast Agriculture University, Harbin, 150030, PR China
| | - Chunyan Li
- School of Resource and Environment, Northeast Agriculture University, Harbin, 150030, PR China
| | - Wulin Yang
- College of Environmental Sciences and Engineering, Peking University, Beijing, 100871, PR China
| |
Collapse
|
18
|
Zhang Y, Lin X, Zhai W, Shen Y, Chen S, Zhang Y, Yu Y, He X, Liu W. Machine Learning on Microstructure-Property Relationship of Lithium-Ion Conducting Oxide Solid Electrolytes. NANO LETTERS 2024; 24:5292-5300. [PMID: 38648075 DOI: 10.1021/acs.nanolett.4c00902] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/25/2024]
Abstract
Understanding the structure-property relationship of lithium-ion conducting solid oxide electrolytes is essential to accelerate their development and commercialization. However, the structural complexity of nonideal materials increases the difficulty of study. Here, we develop an algorithmic framework to understand the effect of microstructure on the properties by linking the microscopic morphology images to their ionic conductivities. We adopt garnet and perovskite polycrystalline oxides as examples and quantify the microscopic morphologies via extracting determined physical parameters from the images. It directly visualizes the effect of physical parameters on their corresponding ionic conductivities. As a result, we can determine the microstructural features of a Li-ion conductor with high ionic conductivity, which can guide the synthesis of highly conductive solid electrolytes. Our work provides a novel approach to understanding the microstructure-property relationship for solid-state ionic materials, showing the potential to extend to other structural/functional ceramics with various physical properties in other fields.
Collapse
Affiliation(s)
- Yue Zhang
- School of Physical Science and Technology, ShanghaiTech University, Shanghai 201210, China
- Shanghai Key Laboratory of High-resolution Electron Microscopy, ShanghaiTech University, Shanghai 201210, China
| | - Xiaoyu Lin
- School of Information Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Wenbo Zhai
- School of Physical Science and Technology, ShanghaiTech University, Shanghai 201210, China
- Shanghai Key Laboratory of High-resolution Electron Microscopy, ShanghaiTech University, Shanghai 201210, China
| | - Yanran Shen
- School of Physical Science and Technology, ShanghaiTech University, Shanghai 201210, China
- Shanghai Key Laboratory of High-resolution Electron Microscopy, ShanghaiTech University, Shanghai 201210, China
| | - Shaojie Chen
- School of Physical Science and Technology, ShanghaiTech University, Shanghai 201210, China
- Shanghai Key Laboratory of High-resolution Electron Microscopy, ShanghaiTech University, Shanghai 201210, China
| | - Yining Zhang
- School of Physical Science and Technology, ShanghaiTech University, Shanghai 201210, China
- Shanghai Key Laboratory of High-resolution Electron Microscopy, ShanghaiTech University, Shanghai 201210, China
| | - Yi Yu
- School of Physical Science and Technology, ShanghaiTech University, Shanghai 201210, China
- Shanghai Key Laboratory of High-resolution Electron Microscopy, ShanghaiTech University, Shanghai 201210, China
| | - Xuming He
- Shanghai Key Laboratory of High-resolution Electron Microscopy, ShanghaiTech University, Shanghai 201210, China
- School of Information Science and Technology, ShanghaiTech University, Shanghai 201210, China
- Shanghai Engineering Research Center of Intelligent Vision and Imaging, Shanghai 201210, China
| | - Wei Liu
- School of Physical Science and Technology, ShanghaiTech University, Shanghai 201210, China
- Shanghai Key Laboratory of High-resolution Electron Microscopy, ShanghaiTech University, Shanghai 201210, China
| |
Collapse
|
19
|
Wang Y, Sorkun MC, Brocks G, Er S. ML-Aided Computational Screening of 2D Materials for Photocatalytic Water Splitting. J Phys Chem Lett 2024:4983-4991. [PMID: 38691841 DOI: 10.1021/acs.jpclett.4c00425] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/03/2024]
Abstract
The exploration of two-dimensional (2D) materials with exceptional physical and chemical properties is essential for the advancement of solar water splitting technologies. However, the discovery of 2D materials is currently heavily reliant on fragmented studies with limited opportunities for fine-tuning the chemical composition and electronic features of compounds. Starting from the V2DB digital library as a resource of 2D materials, we set up and execute a funnel approach that incorporates multiple screening steps to uncover potential candidates for photocatalytic water splitting. The initial screening step is based upon machine learning (ML) predicted properties, and subsequent steps involve first-principles modeling of increasing complexity, going from density functional theory (DFT) to hybrid-DFT to GW calculations. Ensuring that at each stage more complex calculations are only applied to the most promising candidates, our study introduces an effective screening methodology that may serve as a model for accelerating 2D materials discovery within a large chemical space. Our screening process yields a selection of 11 promising 2D photocatalysts.
Collapse
Affiliation(s)
- Yatong Wang
- DIFFER - Dutch Institute for Fundamental Energy Research, De Zaale 20, Eindhoven 5612 AJ, The Netherlands
- Materials Simulation and Modeling, Department of Applied Physics, Eindhoven University of Technology, Eindhoven 5600 MB, The Netherlands
| | - Murat Cihan Sorkun
- DIFFER - Dutch Institute for Fundamental Energy Research, De Zaale 20, Eindhoven 5612 AJ, The Netherlands
| | - Geert Brocks
- Materials Simulation and Modeling, Department of Applied Physics, Eindhoven University of Technology, Eindhoven 5600 MB, The Netherlands
- Computational Chemical Physics, Faculty of Science and Technology and MESA+ Institute for Nanotechnology, University of Twente, P.O. Box 217, 7500 AE Enschede, The Netherlands
| | - Süleyman Er
- DIFFER - Dutch Institute for Fundamental Energy Research, De Zaale 20, Eindhoven 5612 AJ, The Netherlands
| |
Collapse
|
20
|
Jiang J, Li Y, Zhang R, Liu Y. INTransformer: Data augmentation-based contrastive learning by injecting noise into transformer for molecular property prediction. J Mol Graph Model 2024; 128:108703. [PMID: 38228013 DOI: 10.1016/j.jmgm.2024.108703] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 12/05/2023] [Accepted: 01/02/2024] [Indexed: 01/18/2024]
Abstract
Molecular property prediction plays an essential role in drug discovery for identifying the candidate molecules with target properties. Deep learning models usually require sufficient labeled data to train good prediction models. However, the size of labeled data is usually small for molecular property prediction, which brings great challenges to deep learning-based molecular property prediction methods. Furthermore, the global information of molecules is critical for predicting molecular properties. Therefore, we propose INTransformer for molecular property prediction, which is a data augmentation method via contrastive learning to alleviate the limitations of the labeled molecular data while enhancing the ability to capture global information. Specifically, INTransformer consists of two identical Transformer sub-encoders to extract the molecular representation from the original SMILES and noisy SMILES respectively, while achieving the goal of data augmentation. To reduce the influence of noise, we use contrastive learning to ensure the molecular encoding of noisy SMILES is consistent with that of the original input so that the molecular representation information can be better extracted by INTransformer. Experiments on various benchmark datasets show that INTransformer achieved competitive performance for molecular property prediction tasks compared with the baselines and state-of-the-art methods.
Collapse
Affiliation(s)
- Jing Jiang
- Key Laboratory of Linguistic and Cultural Computing, Ministry of Education, Northwest Minzu University, Lanzhou 730030, China.
| | - Yachao Li
- Key Laboratory of Linguistic and Cultural Computing, Ministry of Education, Northwest Minzu University, Lanzhou 730030, China.
| | - Ruisheng Zhang
- School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China.
| | - Yunwu Liu
- School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China.
| |
Collapse
|
21
|
Zhang Q, Ren T, Cao K, Xu Z. Advances of machine learning-assisted small extracellular vesicles detection strategy. Biosens Bioelectron 2024; 251:116076. [PMID: 38340580 DOI: 10.1016/j.bios.2024.116076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 01/22/2024] [Accepted: 01/23/2024] [Indexed: 02/12/2024]
Abstract
Detection of extracellular vesicles (EVs), particularly small EVs (sEVs), is of great significance in exploring their physiological characteristics and clinical applications. The heterogeneity of sEVs plays a crucial role in distinguishing different types of cells and diseases. Machine learning, with its exceptional data processing capabilities, offers a solution to overcome the limitations of conventional detection methods for accurately classifying sEV subtypes and sources. Principal component analysis, linear discriminant analysis, partial least squares discriminant analysis, XGBoost, support vector machine, k-nearest neighbor, and deep learning, along with some combined methods such as principal component-linear discriminant analysis, have been successfully applied in the detection and identification of sEVs. This review focuses on machine learning-assisted detection strategies for cell identification and disease prediction via sEVs, and summarizes the integration of these strategies with surface-enhanced Raman scattering, electrochemistry, inductively coupled plasma mass spectrometry and fluorescence. The performance of different machine learning-based detection strategies is compared, and the advantages and limitations of various machine learning models are also evaluated. Finally, we discuss the merits and limitations of the current approaches and briefly outline the perspective of potential research directions in the field of sEV analysis based on machine learning.
Collapse
Affiliation(s)
- Qi Zhang
- Research Center for Analytical Sciences, Northeastern University, Shenyang, 110819, PR China
| | - Tingju Ren
- Research Center for Analytical Sciences, Northeastern University, Shenyang, 110819, PR China
| | - Ke Cao
- Research Center for Analytical Sciences, Northeastern University, Shenyang, 110819, PR China
| | - Zhangrun Xu
- Research Center for Analytical Sciences, Northeastern University, Shenyang, 110819, PR China.
| |
Collapse
|
22
|
Zhuang Z, Barnard AS. Classification of battery compounds using structure-free Mendeleev encodings. J Cheminform 2024; 16:47. [PMID: 38671512 PMCID: PMC11055346 DOI: 10.1186/s13321-024-00836-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Accepted: 03/29/2024] [Indexed: 04/28/2024] Open
Abstract
Machine learning is a valuable tool that can accelerate the discovery and design of materials occupying combinatorial chemical spaces. However, the prerequisite need for vast amounts of training data can be prohibitive when significant resources are needed to characterize or simulate candidate structures. Recent results have shown that structure-free encoding of complex materials, based entirely on chemical compositions, can overcome this impediment and perform well in unsupervised learning tasks. In this study, we extend this exploration to supervised classification, and show how structure-free encoding can accurately predict classes of material compounds for battery applications without time consuming measurement of bonding networks, lattices or densities. SCIENTIFIC CONTRIBUTION: The comprehensive evaluation of structure-free encodings of complex materials in classification tasks, including binary and multi-class separation, inclusive of three classifiers based on different logic function, is measured four metrics and learning curves. The encoding is applied to two data sets from computational and experimental sources, and the outcomes visualised using 5 approaches to confirms the suitability and superiority of Mendeleev encoding. These methods are general and accessible using source software, to provide simple, intuitive and interpretable materials informatics outcomes to accelerate materials design.
Collapse
Affiliation(s)
- Zixin Zhuang
- School of Computing, Australian National University, 145 Science Road, Acton, 2601, ACT, Australia
| | - Amanda S Barnard
- School of Computing, Australian National University, 145 Science Road, Acton, 2601, ACT, Australia.
| |
Collapse
|
23
|
Sun W, You X, Zhao X, Zhang X, Yang C, Zhang F, Yu J, Yang K, Wang J, Xu F, Chang Y, Qu B, Zhao X, He Y, Wang Q, Chen J, Qing G. Precise Capture and Dynamic Release of Circulating Liver Cancer Cells with Dual-Histidine-Based Cell Imprinted Hydrogels. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024:e2402379. [PMID: 38655900 DOI: 10.1002/adma.202402379] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Revised: 04/22/2024] [Indexed: 04/26/2024]
Abstract
Circulating tumor cells (CTCs) detection presents significant advantages in diagnosing liver cancer due to its noninvasiveness, real-time monitoring, and dynamic tracking. However, the clinical application of CTCs-based diagnosis is largely limited by the challenges of capturing low-abundance CTCs within a complex blood environment while ensuring them alive. Here, an ultrastrong ligand, l-histidine-l-histidine (HH), specifically targeting sialylated glycans on the surface of CTCs, is designed. Furthermore, HH is integrated into a cell-imprinted polymer, constructing a hydrogel with precise CTCs imprinting, high elasticity, satisfactory blood compatibility, and robust anti-interference capacities. These features endow the hydrogel with excellent capture efficiency (>95%) for CTCs in peripheral blood, as well as the ability to release CTCs controllably and alive. Clinical tests substantiate the accurate differentiation between liver cancer, cirrhosis, and healthy groups using this method. The remarkable diagnostic accuracy (94%), lossless release of CTCs, material reversibility, and cost-effectiveness ($6.68 per sample) make the HH-based hydrogel a potentially revolutionary technology for liver cancer diagnosis and single-cell analysis.
Collapse
Affiliation(s)
- Wenjing Sun
- School of Life Sciences and Health Engineering, Jiangnan University, Wuxi, 214122, P. R. China
- State Key Laboratory of Medical Proteomics, National Chromatographic R&A Center, CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, 116023, P. R. China
| | - Xin You
- Department of Respiratory Medicine, The Second Hospital of Dalian Medical University, Dalian, 116023, P. R. China
| | - Xinjia Zhao
- State Key Laboratory of Medical Proteomics, National Chromatographic R&A Center, CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, 116023, P. R. China
| | - Xiaoyu Zhang
- State Key Laboratory of Medical Proteomics, National Chromatographic R&A Center, CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, 116023, P. R. China
| | - Chunhui Yang
- Department of Respiratory Medicine, The Second Hospital of Dalian Medical University, Dalian, 116023, P. R. China
| | - Fusheng Zhang
- State Key Laboratory of Medical Proteomics, National Chromatographic R&A Center, CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, 116023, P. R. China
- College of Chemistry and Chemical Engineering, Wuhan Textile University, Wuhan, 430200, P. R. China
| | - Jiaqi Yu
- College of Chemistry and Chemical Engineering, Wuhan Textile University, Wuhan, 430200, P. R. China
| | - Kaiguang Yang
- State Key Laboratory of Medical Proteomics, National Chromatographic R&A Center, CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, 116023, P. R. China
| | - Jixia Wang
- Ganjiang Chinese Medicine Innovation Center, Nanchang, 330000, P. R. China
| | - Fangfang Xu
- Ganjiang Chinese Medicine Innovation Center, Nanchang, 330000, P. R. China
| | - Yongxin Chang
- State Key Laboratory of Medical Proteomics, National Chromatographic R&A Center, CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, 116023, P. R. China
| | - Boxin Qu
- Department of Respiratory Medicine, The Second Hospital of Dalian Medical University, Dalian, 116023, P. R. China
| | - Xinmiao Zhao
- School of Chemistry and Chemical Engineering, Liaoning Normal University, Dalian, 116029, P. R. China
| | - Yuxuan He
- School of Chemistry and Chemical Engineering, Liaoning Normal University, Dalian, 116029, P. R. China
| | - Qi Wang
- Department of Respiratory Medicine, The Second Hospital of Dalian Medical University, Dalian, 116023, P. R. China
| | - Jinghua Chen
- School of Life Sciences and Health Engineering, Jiangnan University, Wuxi, 214122, P. R. China
| | - Guangyan Qing
- State Key Laboratory of Medical Proteomics, National Chromatographic R&A Center, CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, 116023, P. R. China
- College of Chemistry and Chemical Engineering, Wuhan Textile University, Wuhan, 430200, P. R. China
| |
Collapse
|
24
|
Shakiba M, Akimov AV. Machine-Learned Kohn-Sham Hamiltonian Mapping for Nonadiabatic Molecular Dynamics. J Chem Theory Comput 2024; 20:2992-3007. [PMID: 38581699 DOI: 10.1021/acs.jctc.4c00008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/08/2024]
Abstract
In this work, we report a simple, efficient, and scalable machine-learning (ML) approach for mapping non-self-consistent Kohn-Sham Hamiltonians constructed with one kind of density functional to the nearly self-consistent Hamiltonians constructed with another kind of density functional. This approach is designed as a fast surrogate Hamiltonian calculator for use in long nonadiabatic dynamics simulations of large atomistic systems. In this approach, the input and output features are Hamiltonian matrices computed from different levels of theory. We demonstrate that the developed ML-based Hamiltonian mapping method (1) speeds up the calculations by several orders of magnitude, (2) is conceptually simpler than alternative ML approaches, (3) is applicable to different systems and sizes and can be used for mapping Hamiltonians constructed with arbitrary density functionals, (4) requires a modest training data, learns fast, and generates molecular orbitals and their energies with the accuracy nearly matching that of conventional calculations, and (5) when applied to nonadiabatic dynamics simulation of excitation energy relaxation in large systems yields the corresponding time scales within the margin of error of the conventional calculations. Using this approach, we explore the excitation energy relaxation in C60 fullerene and Si75H64 quantum dot structures and derive qualitative and quantitative insights into dynamics in these systems.
Collapse
Affiliation(s)
- Mohammad Shakiba
- Department of Chemistry, University at Buffalo, The State University of New York, Buffalo, New York 14260, United States
| | - Alexey V Akimov
- Department of Chemistry, University at Buffalo, The State University of New York, Buffalo, New York 14260, United States
| |
Collapse
|
25
|
Pan X, Snyder R, Wang JN, Lander C, Wickizer C, Van R, Chesney A, Xue Y, Mao Y, Mei Y, Pu J, Shao Y. Training machine learning potentials for reactive systems: A Colab tutorial on basic models. J Comput Chem 2024; 45:638-647. [PMID: 38082539 PMCID: PMC10923003 DOI: 10.1002/jcc.27269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 11/10/2023] [Accepted: 11/11/2023] [Indexed: 01/18/2024]
Abstract
In the last several years, there has been a surge in the development of machine learning potential (MLP) models for describing molecular systems. We are interested in a particular area of this field - the training of system-specific MLPs for reactive systems - with the goal of using these MLPs to accelerate free energy simulations of chemical and enzyme reactions. To help new members in our labs become familiar with the basic techniques, we have put together a self-guided Colab tutorial (https://cc-ats.github.io/mlp_tutorial/), which we expect to be also useful to other young researchers in the community. Our tutorial begins with the introduction of simple feedforward neural network (FNN) and kernel-based (using Gaussian process regression, GPR) models by fitting the two-dimensional Müller-Brown potential. Subsequently, two simple descriptors are presented for extracting features of molecular systems: symmetry functions (including the ANI variant) and embedding neural networks (such as DeepPot-SE). Lastly, these features will be fed into FNN and GPR models to reproduce the energies and forces for the molecular configurations in a Claisen rearrangement reaction.
Collapse
Affiliation(s)
- Xiaoliang Pan
- Department of Chemistry and Biochemistry, University of Oklahoma, Norman, OK 73019, USA
| | - Ryan Snyder
- Department of Chemistry and Chemical Biology, Indiana University-Purdue University Indianapolis, Indianapolis, IN 46202, USA
| | - Jia-Ning Wang
- State Key Laboratory of Precision Spectroscopy, School of Physics and Electronic Science, East China Normal University, Shanghai 200241, China
| | - Chance Lander
- Department of Chemistry and Biochemistry, University of Oklahoma, Norman, OK 73019, USA
| | - Carly Wickizer
- Department of Chemistry and Biochemistry, University of Oklahoma, Norman, OK 73019, USA
| | - Richard Van
- Department of Chemistry and Biochemistry, University of Oklahoma, Norman, OK 73019, USA
- Laboratory of Computational Biology, National, Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD 20824, USA
| | - Andrew Chesney
- Department of Chemistry and Biochemistry, University of Oklahoma, Norman, OK 73019, USA
| | - Yuanfei Xue
- State Key Laboratory of Precision Spectroscopy, School of Physics and Electronic Science, East China Normal University, Shanghai 200241, China
| | - Yuezhi Mao
- Department of Chemistry and Biochemistry, San Diego State University, San Diego, CA 92182, USA
| | - Ye Mei
- State Key Laboratory of Precision Spectroscopy, School of Physics and Electronic Science, East China Normal University, Shanghai 200241, China
- NYU-ECNU Center for Computational Chemistry at NYU Shanghai, Shanghai 200062, China
- Collaborative Innovation Center of Extreme Optics, Shanxi University, Taiyuan, Shanxi 030006, China
| | - Jingzhi Pu
- Department of Chemistry and Chemical Biology, Indiana University-Purdue University Indianapolis, Indianapolis, IN 46202, USA
| | - Yihan Shao
- Department of Chemistry and Biochemistry, University of Oklahoma, Norman, OK 73019, USA
| |
Collapse
|
26
|
Hirai H. Practical application of quantum neural network to materials informatics. Sci Rep 2024; 14:8583. [PMID: 38615092 PMCID: PMC11016107 DOI: 10.1038/s41598-024-59276-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Accepted: 04/09/2024] [Indexed: 04/15/2024] Open
Abstract
Quantum neural network (QNN) models have received increasing attention owing to their strong expressibility and resistance to overfitting. It is particularly useful when the size of the training data is small, making it a good fit for materials informatics (MI) problems. However, there are only a few examples of the application of QNN to multivariate regression models, and little is known about how these models are constructed. This study aims to construct a QNN model to predict the melting points of metal oxides as an example of a multivariate regression task for the MI problem. Different architectures (encoding methods and entangler arrangements) are explored to create an effective QNN model. Shallow-depth ansatzs could achieve sufficient expressibility using sufficiently entangled circuits. The "linear" entangler was adequate for providing the necessary entanglement. The expressibility of the QNN model could be further improved by increasing the circuit width. The generalization performance could also be improved, outperforming the classical NN model. No overfitting was observed in the QNN models with a well-designed encoder. These findings suggest that QNN can be a useful tool for MI.
Collapse
Affiliation(s)
- Hirotoshi Hirai
- Toyota Central R&D Labs., Inc., 41-1, Yokomichi, Nagakute, Aichi, 480-1192, Japan.
| |
Collapse
|
27
|
Glass S, Schmidt M, Merten P, Abdul Latif A, Fischer K, Schulze A, Friederich P, Filiz V. Design of Modified Polymer Membranes Using Machine Learning. ACS APPLIED MATERIALS & INTERFACES 2024; 16. [PMID: 38600824 PMCID: PMC11056926 DOI: 10.1021/acsami.3c18805] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Revised: 03/19/2024] [Accepted: 03/21/2024] [Indexed: 04/12/2024]
Abstract
Surface modification is an attractive strategy to adjust the properties of polymer membranes. Unfortunately, predictive structure-processing-property relationships between the modification strategies and membrane performance are often unknown. One possibility to tackle this challenge is the application of data-driven methods such as machine learning. In this study, we applied machine learning methods to data sets containing the performance parameters of modified membranes. The resulting machine learning models were used to predict performance parameters, such as the pure water permeability and the zeta potential of membranes modified with new substances. The predictions had low prediction errors, which allowed us to generalize them to similar membrane modifications and processing conditions. Additionally, machine learning methods were able to identify the impact of substance properties and process parameters on the resulting membrane properties. Our results demonstrate that small data sets, as they are common in materials science, can be used as training data for predictive machine learning models. Therefore, machine learning shows great potential as a tool to expedite the development of high-performance membranes while reducing the time and costs associated with the development process at the same time.
Collapse
Affiliation(s)
- Sarah Glass
- Institute
of Membrane Research, Helmholtz-Zentrum
Hereon, Max-Planck-Str.
1, Geesthacht 21502, Germany
- Institute
of Theoretical Informatics, Karlsruhe Institute
of Technology (KIT), Kaiserstr. 12, 76131 Karlsruhe, Germany
| | - Martin Schmidt
- Leibniz
Institute of Surface Engineering (IOM), Permoserstr. 15, Leipzig 04318, Germany
| | - Petra Merten
- Institute
of Membrane Research, Helmholtz-Zentrum
Hereon, Max-Planck-Str.
1, Geesthacht 21502, Germany
| | - Amira Abdul Latif
- Leibniz
Institute of Surface Engineering (IOM), Permoserstr. 15, Leipzig 04318, Germany
| | - Kristina Fischer
- Leibniz
Institute of Surface Engineering (IOM), Permoserstr. 15, Leipzig 04318, Germany
| | - Agnes Schulze
- Leibniz
Institute of Surface Engineering (IOM), Permoserstr. 15, Leipzig 04318, Germany
| | - Pascal Friederich
- Institute
of Theoretical Informatics, Karlsruhe Institute
of Technology (KIT), Kaiserstr. 12, 76131 Karlsruhe, Germany
- Institute
of Nanotechnology, Karlsruhe Institute of
Technology (KIT), Kaiserstr.
12, 76131 Karlsruhe, Germany
| | - Volkan Filiz
- Institute
of Membrane Research, Helmholtz-Zentrum
Hereon, Max-Planck-Str.
1, Geesthacht 21502, Germany
| |
Collapse
|
28
|
Zhu C, Bamidele EA, Shen X, Zhu G, Li B. Machine Learning Aided Design and Optimization of Thermal Metamaterials. Chem Rev 2024; 124:4258-4331. [PMID: 38546632 PMCID: PMC11009967 DOI: 10.1021/acs.chemrev.3c00708] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 01/31/2024] [Accepted: 02/08/2024] [Indexed: 04/11/2024]
Abstract
Artificial Intelligence (AI) has advanced material research that were previously intractable, for example, the machine learning (ML) has been able to predict some unprecedented thermal properties. In this review, we first elucidate the methodologies underpinning discriminative and generative models, as well as the paradigm of optimization approaches. Then, we present a series of case studies showcasing the application of machine learning in thermal metamaterial design. Finally, we give a brief discussion on the challenges and opportunities in this fast developing field. In particular, this review provides: (1) Optimization of thermal metamaterials using optimization algorithms to achieve specific target properties. (2) Integration of discriminative models with optimization algorithms to enhance computational efficiency. (3) Generative models for the structural design and optimization of thermal metamaterials.
Collapse
Affiliation(s)
- Changliang Zhu
- Department
of Materials Science and Engineering, Southern
University of Science and Technology, Shenzhen 518055, P.R. China
| | - Emmanuel Anuoluwa Bamidele
- Materials
Science and Engineering Program, University
of Colorado, Boulder, Colorado 80309, United States
| | - Xiangying Shen
- Department
of Materials Science and Engineering, Southern
University of Science and Technology, Shenzhen 518055, P.R. China
| | - Guimei Zhu
- School
of Microelectronics, Southern University
of Science and Technology, Shenzhen 518055, P.R. China
| | - Baowen Li
- Department
of Materials Science and Engineering, Southern
University of Science and Technology, Shenzhen 518055, P.R. China
- School
of Microelectronics, Southern University
of Science and Technology, Shenzhen 518055, P.R. China
- Department
of Physics, Southern University of Science
and Technology, Shenzhen 518055, P.R. China
- Shenzhen
International Quantum Academy, Shenzhen 518048, P.R. China
- Paul M. Rady
Department of Mechanical Engineering and Department of Physics, University of Colorado, Boulder 80309, United States
| |
Collapse
|
29
|
Kiyohara S, Hinuma Y, Oba F. Band Alignment of Oxides by Learnable Structural-Descriptor-Aided Neural Network and Transfer Learning. J Am Chem Soc 2024; 146:9697-9708. [PMID: 38546127 PMCID: PMC11009958 DOI: 10.1021/jacs.3c13574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2023] [Revised: 02/25/2024] [Accepted: 02/27/2024] [Indexed: 04/11/2024]
Abstract
The band alignment of semiconductors, insulators, and dielectrics is relevant to diverse material properties and device structures utilizing their surfaces and interfaces. In particular, the ionization potential and electron affinity are fundamental quantities that describe surface-dependent band-edge positions with respect to the vacuum level. Their accurate and systematic determination, however, demands elaborate experiments or simulations for well-characterized surfaces. Here, we report machine learning for the band alignment of nonmetallic oxides using a high-throughput first-principles calculation data set containing about 3000 oxide surfaces. Our neural network accurately predicts the band positions for relaxed surfaces of binary oxides simply by using the information on bulk structures and surface termination planes. Moreover, we extend the model to naturally include multiple-cation effects and transfer it to ternary oxides. The present approach enables the band alignment of a vast number of solid surfaces, thereby opening the way to a systematic understanding and materials screening.
Collapse
Affiliation(s)
- Shin Kiyohara
- Laboratory
for Materials and Structures, Institute of Innovative Research, Tokyo Institute of Technology, R3-7, 4259 Nagatsuta, Midori-ku, Yokohama 226-8501, Japan
- Institute
for Materials Research, Tohoku University, 2-2-1 Katahira,
Aoba-ku, Sendai 980-8577, Japan
| | - Yoyo Hinuma
- Department
of Energy and Environment, National Institute
of Advanced Industrial Science and Technology (AIST), 1-8-31 Midorigaoka, Ikeda, Osaka 563-8577, Japan
| | - Fumiyasu Oba
- Laboratory
for Materials and Structures, Institute of Innovative Research, Tokyo Institute of Technology, R3-7, 4259 Nagatsuta, Midori-ku, Yokohama 226-8501, Japan
- MDX
Research Center for Element Strategy, International Research Frontiers
Initiative, Tokyo Institute of Technology, SE-6, 4259 Nagatsuta, Midori-ku, Yokohama 226-8501, Japan
| |
Collapse
|
30
|
Schwade M, Schilcher MJ, Reverón Baecker C, Grumet M, Egger DA. Temperature-transferable tight-binding model using a hybrid-orbital basis. J Chem Phys 2024; 160:134102. [PMID: 38557853 DOI: 10.1063/5.0197986] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Accepted: 03/13/2024] [Indexed: 04/04/2024] Open
Abstract
Finite-temperature calculations are relevant for rationalizing material properties, yet they are computationally expensive because large system sizes or long simulation times are typically required. Circumventing the need for performing many explicit first-principles calculations, tight-binding and machine-learning models for the electronic structure emerged as promising alternatives, but transferability of such methods to elevated temperatures in a data-efficient way remains a great challenge. In this work, we suggest a tight-binding model for efficient and accurate calculations of temperature-dependent properties of semiconductors. Our approach utilizes physics-informed modeling of the electronic structure in the form of hybrid-orbital basis functions and numerically integrating atomic orbitals for the distance dependence of matrix elements. We show that these design choices lead to a tight-binding model with a minimal amount of parameters that are straightforwardly optimized using density functional theory or alternative electronic-structure methods. The temperature transferability of our model is tested by applying it to existing molecular-dynamics trajectories without explicitly fitting temperature-dependent data and comparison with density functional theory. We utilize it together with machine-learning molecular dynamics and hybrid density functional theory for the prototypical semiconductor gallium arsenide. We find that including the effects of thermal expansion on the onsite terms of the tight-binding model is important in order to accurately describe electronic properties at elevated temperatures in comparison with experiment.
Collapse
Affiliation(s)
- Martin Schwade
- Physics Department, TUM School of Natural Sciences, Technical University of Munich, 85748 Garching, Germany
| | - Maximilian J Schilcher
- Physics Department, TUM School of Natural Sciences, Technical University of Munich, 85748 Garching, Germany
| | - Christian Reverón Baecker
- Physics Department, TUM School of Natural Sciences, Technical University of Munich, 85748 Garching, Germany
| | - Manuel Grumet
- Physics Department, TUM School of Natural Sciences, Technical University of Munich, 85748 Garching, Germany
| | - David A Egger
- Physics Department, TUM School of Natural Sciences, Technical University of Munich, 85748 Garching, Germany
| |
Collapse
|
31
|
Fan M, Wen T, Chen S, Dong Y, Wang CA. Perspectives Toward Damage-Tolerant Nanostructure Ceramics. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024:e2309834. [PMID: 38582503 DOI: 10.1002/advs.202309834] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Revised: 03/13/2024] [Indexed: 04/08/2024]
Abstract
Advanced ceramic materials and devices call for better reliability and damage tolerance. In addition to their strong bonding nature, there are examples demonstrating superior mechanical properties of nanostructure ceramics, such as damage-tolerant ceramic aerogels that can withstand high deformation without cracking and local plasticity in dense nanocrystalline ceramics. The recent progresses shall be reviewed in this perspective article. Three topics including highly elastic nano-fibrous ceramic aerogels, load-bearing nanoceramics with improved mechanical properties, and implementing machine learning-assisted simulations toolbox in understanding the relationship among structure, deformation mechanisms, and microstructure-properties shall be discussed. It is hoped that the perspectives present here can help the discovery, synthesis, and processing of future structural ceramic materials that are insensitive to processing flaws and local damages in service.
Collapse
Affiliation(s)
- Meicen Fan
- State Key Lab of New Ceramics and Fine Processing, School of Materials Science and Engineering, Tsinghua University, Beijing, 100084, China
| | - Tongqi Wen
- Department of Mechanical Engineering, The University of Hong Kong, Hong Kong, SAR, China
| | - Shile Chen
- State Key Lab of New Ceramics and Fine Processing, School of Materials Science and Engineering, Tsinghua University, Beijing, 100084, China
| | - Yanhao Dong
- State Key Lab of New Ceramics and Fine Processing, School of Materials Science and Engineering, Tsinghua University, Beijing, 100084, China
| | - Chang-An Wang
- State Key Lab of New Ceramics and Fine Processing, School of Materials Science and Engineering, Tsinghua University, Beijing, 100084, China
| |
Collapse
|
32
|
Chen X, Gao Y, Wang L, Cui W, Huang J, Du Y, Wang B. Large language model enhanced corpus of CO 2 reduction electrocatalysts and synthesis procedures. Sci Data 2024; 11:347. [PMID: 38582751 PMCID: PMC10998834 DOI: 10.1038/s41597-024-03180-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Accepted: 03/22/2024] [Indexed: 04/08/2024] Open
Abstract
CO2 electroreduction has garnered significant attention from both the academic and industrial communities. Extracting crucial information related to catalysts from domain literature can help scientists find new and effective electrocatalysts. Herein, we used various advanced machine learning, natural language processing techniques and large language models (LLMs) approaches to extract relevant information about the CO2 electrocatalytic reduction process from scientific literature. By applying the extraction pipeline, we present an open-source corpus for electrocatalytic CO2 reduction. The database contains two types of corpus: (1) the benchmark corpus, which is a collection of 6,985 records extracted from 1,081 publications by catalysis postgraduates; and (2) the extended corpus, which consists of content extracted from 5,941 documents using traditional NLP techniques and LLMs techniques. The Extended Corpus I and II contain 77,016 and 30,283 records, respectively. Furthermore, several domain literature fine-tuned LLMs were developed. Overall, this work will contribute to the exploration of new and effective electrocatalysts by leveraging information from domain literature using cutting-edge computer techniques.
Collapse
Affiliation(s)
- Xueqing Chen
- Laboratory of Big Data Knowledge, Computer Network Information Center, Chinese Academy of Sciences, Beijing, 100083, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Yang Gao
- CAS Key Laboratory of Nanosystem and Hierarchical Fabrication, National Center for Nanoscience and Technology (NCNST), Beijing, 100190, China
| | - Ludi Wang
- Laboratory of Big Data Knowledge, Computer Network Information Center, Chinese Academy of Sciences, Beijing, 100083, China
| | - Wenjuan Cui
- Laboratory of Big Data Knowledge, Computer Network Information Center, Chinese Academy of Sciences, Beijing, 100083, China
| | - Jiamin Huang
- CAS Key Laboratory of Nanosystem and Hierarchical Fabrication, National Center for Nanoscience and Technology (NCNST), Beijing, 100190, China
| | - Yi Du
- Laboratory of Big Data Knowledge, Computer Network Information Center, Chinese Academy of Sciences, Beijing, 100083, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- Hangzhou Institute for Advanced Study, UCAS, Hangzhou, 310000, China.
| | - Bin Wang
- CAS Key Laboratory of Nanosystem and Hierarchical Fabrication, National Center for Nanoscience and Technology (NCNST), Beijing, 100190, China.
| |
Collapse
|
33
|
Unke OT, Stöhr M, Ganscha S, Unterthiner T, Maennel H, Kashubin S, Ahlin D, Gastegger M, Medrano Sandonas L, Berryman JT, Tkatchenko A, Müller KR. Biomolecular dynamics with machine-learned quantum-mechanical force fields trained on diverse chemical fragments. SCIENCE ADVANCES 2024; 10:eadn4397. [PMID: 38579003 DOI: 10.1126/sciadv.adn4397] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/10/2023] [Accepted: 02/29/2024] [Indexed: 04/07/2024]
Abstract
The GEMS method enables molecular dynamics simulations of large heterogeneous systems at ab initio quality.
Collapse
Affiliation(s)
- Oliver T Unke
- Google DeepMind, Tucholskystraße 2, 10117 Berlin, Germany and Brandschenkestrasse 110, 8002 Zürich, Switzerland
- Machine Learning Group, Technische Universität Berlin, 10587 Berlin, Germany
- DFG Cluster of Excellence "Unifying Systems in Catalysis" (UniSysCat), Technische Universität Berlin, 10623 Berlin, Germany
| | - Martin Stöhr
- Department of Physics and Materials Science, University of Luxembourg, L-1511 Luxembourg City, Luxembourg
| | - Stefan Ganscha
- Google DeepMind, Tucholskystraße 2, 10117 Berlin, Germany and Brandschenkestrasse 110, 8002 Zürich, Switzerland
| | - Thomas Unterthiner
- Google DeepMind, Tucholskystraße 2, 10117 Berlin, Germany and Brandschenkestrasse 110, 8002 Zürich, Switzerland
| | - Hartmut Maennel
- Google DeepMind, Tucholskystraße 2, 10117 Berlin, Germany and Brandschenkestrasse 110, 8002 Zürich, Switzerland
| | - Sergii Kashubin
- Google DeepMind, Tucholskystraße 2, 10117 Berlin, Germany and Brandschenkestrasse 110, 8002 Zürich, Switzerland
| | - Daniel Ahlin
- Google DeepMind, Tucholskystraße 2, 10117 Berlin, Germany and Brandschenkestrasse 110, 8002 Zürich, Switzerland
| | - Michael Gastegger
- Machine Learning Group, Technische Universität Berlin, 10587 Berlin, Germany
- DFG Cluster of Excellence "Unifying Systems in Catalysis" (UniSysCat), Technische Universität Berlin, 10623 Berlin, Germany
- BASLEARN - TU Berlin/BASF Joint Lab for Machine Learning, Technische Universität Berlin, 10587 Berlin, Germany
| | - Leonardo Medrano Sandonas
- Department of Physics and Materials Science, University of Luxembourg, L-1511 Luxembourg City, Luxembourg
| | - Joshua T Berryman
- Department of Physics and Materials Science, University of Luxembourg, L-1511 Luxembourg City, Luxembourg
| | - Alexandre Tkatchenko
- Department of Physics and Materials Science, University of Luxembourg, L-1511 Luxembourg City, Luxembourg
| | - Klaus-Robert Müller
- Google DeepMind, Tucholskystraße 2, 10117 Berlin, Germany and Brandschenkestrasse 110, 8002 Zürich, Switzerland
- Machine Learning Group, Technische Universität Berlin, 10587 Berlin, Germany
- Department of Artificial Intelligence, Korea University, Anam-dong, Seongbuk-gu, Seoul 02841, Korea
- Max Planck Institute for Informatics, Stuhlsatzenhausweg, 66123 Saarbrücken, Germany
- BIFOLD - Berlin Institute for the Foundations of Learning and Data, Berlin, Germany
| |
Collapse
|
34
|
Wesołowski P, Wales DJ, Pracht P. Multilevel Framework for Analysis of Protein Folding Involving Disulfide Bond Formation. J Phys Chem B 2024; 128:3145-3156. [PMID: 38512062 PMCID: PMC11000224 DOI: 10.1021/acs.jpcb.4c00104] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Revised: 03/06/2024] [Accepted: 03/06/2024] [Indexed: 03/22/2024]
Abstract
In this study, a three-layered multicenter ONIOM approach is implemented to characterize the naive folding pathway of bovine pancreatic trypsin inhibitor (BPTI). Each layer represents a distinct level of theory, where the initial layer, encompassing the entire protein, is modeled by a general all-atom force-field GFN-FF. An intermediate electronic structure layer consisting of three multicenter fragments is introduced with the state-of-the-art semiempirical tight-binding method GFN2-xTB. Higher accuracy, specifically addressing the breaking and formation of the three disulfide bonds, is achieved at the innermost layer using the composite DFT method r2SCAN-3c. Our analysis sheds light on the structural stability of BPTI, particularly the significance of interlinking disulfide bonds. The accuracy and efficiency of the multicenter QM/SQM/MM approach are benchmarked using the oxidative formation of cystine. For the folding pathway of BPTI, relative stabilities are investigated through the calculation of free energy contributions for selected intermediates, focusing on the impact of the disulfide bond. Our results highlight the intricate trade-off between accuracy and computational cost, demonstrating that the multicenter ONIOM approach provides a well-balanced and comprehensive solution to describe electronic structure effects in biomolecular systems. We conclude that multiscale energy landscape exploration provides a robust methodology for the study of intriguing biological targets.
Collapse
Affiliation(s)
- Patryk
A. Wesołowski
- Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, U.K.
| | - David J. Wales
- Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, U.K.
| | - Philipp Pracht
- Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, U.K.
| |
Collapse
|
35
|
Kneiding H, Nova A, Balcells D. Directional multiobjective optimization of metal complexes at the billion-system scale. NATURE COMPUTATIONAL SCIENCE 2024; 4:263-273. [PMID: 38553635 DOI: 10.1038/s43588-024-00616-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/01/2023] [Accepted: 02/29/2024] [Indexed: 04/14/2024]
Abstract
The discovery of transition metal complexes (TMCs) with optimal properties requires large ligand libraries and efficient multiobjective optimization algorithms. Here we provide the tmQMg-L library, containing 30k diverse and synthesizable ligands with robustly assigned charges and metal coordination modes. tmQMg-L enabled the generation of 1.37 million palladium TMCs, which were used to develop and benchmark the Pareto-Lighthouse multiobjective genetic algorithm (PL-MOGA). With fine control over aim and scope, this algorithm maximized both the polarizability and highest occupied molecular orbital-lowest unoccupied molecular orbital gap of the TMCs within selected regions of the Pareto front, without requiring prior knowledge on the objective limits. Instead of genetic operations on small ligand fragments, the PL-MOGA did whole-ligand mutation and crossover operations, which in chemical spaces containing billions of systems, yielded thousands of highly diverse TMCs in an interpretable manner.
Collapse
Affiliation(s)
- Hannes Kneiding
- Hylleraas Centre for Quantum Molecular Sciences, Department of Chemistry, University of Oslo, Oslo, Norway
| | - Ainara Nova
- Hylleraas Centre for Quantum Molecular Sciences, Department of Chemistry, University of Oslo, Oslo, Norway
- Centre for Materials Science and Nanotechnology, Department of Chemistry, University of Oslo, Oslo, Norway
| | - David Balcells
- Hylleraas Centre for Quantum Molecular Sciences, Department of Chemistry, University of Oslo, Oslo, Norway.
| |
Collapse
|
36
|
Li Z, Song P, Li G, Han Y, Ren X, Bai L, Su J. AI energized hydrogel design, optimization and application in biomedicine. Mater Today Bio 2024; 25:101014. [PMID: 38464497 PMCID: PMC10924066 DOI: 10.1016/j.mtbio.2024.101014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2024] [Revised: 02/26/2024] [Accepted: 02/28/2024] [Indexed: 03/12/2024] Open
Abstract
Traditional hydrogel design and optimization methods usually rely on repeated experiments, which is time-consuming and expensive, resulting in a slow-moving of advanced hydrogel development. With the rapid development of artificial intelligence (AI) technology and increasing material data, AI-energized design and optimization of hydrogels for biomedical applications has emerged as a revolutionary breakthrough in materials science. This review begins by outlining the history of AI and the potential advantages of using AI in the design and optimization of hydrogels, such as prediction and optimization of properties, multi-attribute optimization, high-throughput screening, automated material discovery, optimizing experimental design, and etc. Then, we focus on the various applications of hydrogels supported by AI technology in biomedicine, including drug delivery, bio-inks for advanced manufacturing, tissue repair, and biosensors, so as to provide a clear and comprehensive understanding of researchers in this field. Finally, we discuss the future directions and prospects, and provide a new perspective for the research and development of novel hydrogel materials for biomedical applications.
Collapse
Affiliation(s)
- Zuhao Li
- Department of Orthopedics, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, China
- Organoid Research Center, Institute of Translational Medicine, Shanghai University, Shanghai, 200444, China
- National Center for Translational Medicine (Shanghai) SHU Branch, Shanghai University, Shanghai, 200444, China
| | - Peiran Song
- Organoid Research Center, Institute of Translational Medicine, Shanghai University, Shanghai, 200444, China
- National Center for Translational Medicine (Shanghai) SHU Branch, Shanghai University, Shanghai, 200444, China
| | - Guangfeng Li
- Organoid Research Center, Institute of Translational Medicine, Shanghai University, Shanghai, 200444, China
- National Center for Translational Medicine (Shanghai) SHU Branch, Shanghai University, Shanghai, 200444, China
| | - Yafei Han
- Organoid Research Center, Institute of Translational Medicine, Shanghai University, Shanghai, 200444, China
- National Center for Translational Medicine (Shanghai) SHU Branch, Shanghai University, Shanghai, 200444, China
| | - Xiaoxiang Ren
- Organoid Research Center, Institute of Translational Medicine, Shanghai University, Shanghai, 200444, China
- National Center for Translational Medicine (Shanghai) SHU Branch, Shanghai University, Shanghai, 200444, China
| | - Long Bai
- Organoid Research Center, Institute of Translational Medicine, Shanghai University, Shanghai, 200444, China
- National Center for Translational Medicine (Shanghai) SHU Branch, Shanghai University, Shanghai, 200444, China
| | - Jiacan Su
- Department of Orthopedics, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, China
- Organoid Research Center, Institute of Translational Medicine, Shanghai University, Shanghai, 200444, China
- National Center for Translational Medicine (Shanghai) SHU Branch, Shanghai University, Shanghai, 200444, China
| |
Collapse
|
37
|
Rosa LAS, Brugnago EL, Delben GJ, Rost JM, Beims MW. The influence of hyperchaoticity, synchronization, and Shannon entropy on the performance of a physical reservoir computer. CHAOS (WOODBURY, N.Y.) 2024; 34:043120. [PMID: 38579146 DOI: 10.1063/5.0175001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Accepted: 03/21/2024] [Indexed: 04/07/2024]
Abstract
In this paper, we analyze the dynamic effect of a reservoir computer (RC) on its performance. Modified Kuramoto's coupled oscillators are used to model the RC, and synchronization, Lyapunov spectrum (and dimension), Shannon entropy, and the upper bound of the Kolmogorov-Sinai entropy are employed to characterize the dynamics of the RC. The performance of the RC is analyzed by reproducing the distribution of random, Gaussian, and quantum jumps series (shelved states) since a replica of the time evolution of a completely random series is not possible to generate. We demonstrate that hyperchaotic motion, moderate Shannon entropy, and a higher degree of synchronization of Kuramoto's oscillators lead to the best performance of the RC. Therefore, an appropriate balance of irregularity and order in the oscillator's dynamics leads to better performances.
Collapse
Affiliation(s)
- Lucas A S Rosa
- Departamento de Física, Universidade Federal do Paraná, 81531-980 Curitiba, Paraná, Brazil
| | - Eduardo L Brugnago
- Instituto de Fí sica, Universidade de São Paulo, 05508-090 São Paulo, SP, Brazil
| | - Guilherme J Delben
- Departamento de Ciências Naturais e Sociais, Universidade Federal de Santa Catarina, 89520-000 Curitibanos, SC, Brazil
| | - Jan-Michael Rost
- Max-Planck Institute for the Physics of Complex Systems, Nöthnitzerstr.38, 01187 Dresden, Germany
| | - Marcus W Beims
- Departamento de Física, Universidade Federal do Paraná, 81531-980 Curitiba, Paraná, Brazil
- Max-Planck Institute for the Physics of Complex Systems, Nöthnitzerstr.38, 01187 Dresden, Germany
| |
Collapse
|
38
|
Zhang R, Wu C, Yang Q, Liu C, Wang Y, Li K, Huang L, Zhou F. MolFeSCue: enhancing molecular property prediction in data-limited and imbalanced contexts using few-shot and contrastive learning. Bioinformatics 2024; 40:btae118. [PMID: 38426310 PMCID: PMC10984949 DOI: 10.1093/bioinformatics/btae118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2023] [Revised: 02/04/2024] [Accepted: 02/27/2024] [Indexed: 03/02/2024] Open
Abstract
MOTIVATION Predicting molecular properties is a pivotal task in various scientific domains, including drug discovery, material science, and computational chemistry. This problem is often hindered by the lack of annotated data and imbalanced class distributions, which pose significant challenges in developing accurate and robust predictive models. RESULTS This study tackles these issues by employing pretrained molecular models within a few-shot learning framework. A novel dynamic contrastive loss function is utilized to further improve model performance in the situation of class imbalance. The proposed MolFeSCue framework not only facilitates rapid generalization from minimal samples, but also employs a contrastive loss function to extract meaningful molecular representations from imbalanced datasets. Extensive evaluations and comparisons of MolFeSCue and state-of-the-art algorithms have been conducted on multiple benchmark datasets, and the experimental data demonstrate our algorithm's effectiveness in molecular representations and its broad applicability across various pretrained models. Our findings underscore MolFeSCues potential to accelerate advancements in drug discovery. AVAILABILITY AND IMPLEMENTATION We have made all the source code utilized in this study publicly accessible via GitHub at http://www.healthinformaticslab.org/supp/ or https://github.com/zhangruochi/MolFeSCue. The code (MolFeSCue-v1-00) is also available as the supplementary file of this paper.
Collapse
Affiliation(s)
- Ruochi Zhang
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin 130012, China
- School of Artificial Intelligence, Jilin University, Changchun 130012, China
| | - Chao Wu
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin 130012, China
- College of Computer Science and Technology, Jilin University, Changchun, Jilin 130012, China
| | - Qian Yang
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin 130012, China
- College of Computer Science and Technology, Jilin University, Changchun, Jilin 130012, China
| | - Chang Liu
- Beijing Life Science Academy, Beijing 102209, China
| | - Yan Wang
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin 130012, China
- College of Computer Science and Technology, Jilin University, Changchun, Jilin 130012, China
| | - Kewei Li
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin 130012, China
- College of Computer Science and Technology, Jilin University, Changchun, Jilin 130012, China
| | - Lan Huang
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin 130012, China
- College of Computer Science and Technology, Jilin University, Changchun, Jilin 130012, China
| | - Fengfeng Zhou
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin 130012, China
- College of Computer Science and Technology, Jilin University, Changchun, Jilin 130012, China
- School of Biology and Engineering, Guizhou Medical University, Guiyang, Guizhou 550025, China
| |
Collapse
|
39
|
Liu S. Harvesting Chemical Understanding with Machine Learning and Quantum Computers. ACS PHYSICAL CHEMISTRY AU 2024; 4:135-142. [PMID: 38560751 PMCID: PMC10979482 DOI: 10.1021/acsphyschemau.3c00067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 12/29/2023] [Accepted: 01/02/2024] [Indexed: 04/04/2024]
Abstract
It is tenable to argue that nobody can predict the future with certainty, yet one can learn from the past and make informed projections for the years ahead. In this Perspective, we overview the status of how theory and computation can be exploited to obtain chemical understanding from wave function theory and density functional theory, and then outlook the likely impact of machine learning (ML) and quantum computers (QC) to appreciate traditional chemical concepts in decades to come. It is maintained that the development and maturation of ML and QC methods in theoretical and computational chemistry represent two paradigm shifts about how the Schrödinger equation can be solved. New chemical understanding can be harnessed in these two new paradigms by making respective use of ML features and QC qubits. Before that happens, however, we still have hurdles to face and obstacles to overcome in both ML and QC arenas. Possible pathways to tackle these challenges are proposed. We anticipate that hierarchical modeling, in contrast to multiscale modeling, will emerge and thrive, becoming the workhorse of in silico simulations in the next few decades.
Collapse
|
40
|
Jiang T, Wang Z, Yu W, Wang J, Yu S, Bao X, Wei B, Xuan Q. Mix-Key: graph mixup with key structures for molecular property prediction. Brief Bioinform 2024; 25:bbae165. [PMID: 38706318 PMCID: PMC11070654 DOI: 10.1093/bib/bbae165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 02/21/2024] [Accepted: 04/04/2024] [Indexed: 05/07/2024] Open
Abstract
Molecular property prediction faces the challenge of limited labeled data as it necessitates a series of specialized experiments to annotate target molecules. Data augmentation techniques can effectively address the issue of data scarcity. In recent years, Mixup has achieved significant success in traditional domains such as image processing. However, its application in molecular property prediction is relatively limited due to the irregular, non-Euclidean nature of graphs and the fact that minor variations in molecular structures can lead to alterations in their properties. To address these challenges, we propose a novel data augmentation method called Mix-Key tailored for molecular property prediction. Mix-Key aims to capture crucial features of molecular graphs, focusing separately on the molecular scaffolds and functional groups. By generating isomers that are relatively invariant to the scaffolds or functional groups, we effectively preserve the core information of molecules. Additionally, to capture interactive information between the scaffolds and functional groups while ensuring correlation between the original and augmented graphs, we introduce molecular fingerprint similarity and node similarity. Through these steps, Mix-Key determines the mixup ratio between the original graph and two isomers, thus generating more informative augmented molecular graphs. We extensively validate our approach on molecular datasets of different scales with several Graph Neural Network architectures. The results demonstrate that Mix-Key consistently outperforms other data augmentation methods in enhancing molecular property prediction on several datasets.
Collapse
Affiliation(s)
- Tianyi Jiang
- Institute of Cyberspace Security, College of Information Engineering, Zhejiang University of Technology, 310023, Hangzhou, China
- Binjiang Institute of Artificial Intelligence, Zhejiang University of Technology, 310056, Hangzhou, China
| | - Zeyu Wang
- Institute of Cyberspace Security, College of Information Engineering, Zhejiang University of Technology, 310023, Hangzhou, China
- Binjiang Institute of Artificial Intelligence, Zhejiang University of Technology, 310056, Hangzhou, China
| | - Wenchao Yu
- the College of Pharmaceutical Science & Collaborative Innovation Center of Yangtze River Delta Region Green Pharmaceuticals, Zhejiang University of Technology, 310014, Hangzhou, China
| | - Jinhuan Wang
- Institute of Cyberspace Security, College of Information Engineering, Zhejiang University of Technology, 310023, Hangzhou, China
- Binjiang Institute of Artificial Intelligence, Zhejiang University of Technology, 310056, Hangzhou, China
| | - Shanqing Yu
- Institute of Cyberspace Security, College of Information Engineering, Zhejiang University of Technology, 310023, Hangzhou, China
- Binjiang Institute of Artificial Intelligence, Zhejiang University of Technology, 310056, Hangzhou, China
| | - Xiaoze Bao
- the College of Pharmaceutical Science & Collaborative Innovation Center of Yangtze River Delta Region Green Pharmaceuticals, Zhejiang University of Technology, 310014, Hangzhou, China
| | - Bin Wei
- the College of Pharmaceutical Science & Collaborative Innovation Center of Yangtze River Delta Region Green Pharmaceuticals, Zhejiang University of Technology, 310014, Hangzhou, China
| | - Qi Xuan
- Institute of Cyberspace Security, College of Information Engineering, Zhejiang University of Technology, 310023, Hangzhou, China
- Binjiang Institute of Artificial Intelligence, Zhejiang University of Technology, 310056, Hangzhou, China
| |
Collapse
|
41
|
Hsu T, Sadigh B, Bulatov V, Zhou F. Score Dynamics: Scaling Molecular Dynamics with Picoseconds Time Steps via Conditional Diffusion Model. J Chem Theory Comput 2024; 20:2335-2348. [PMID: 38489243 DOI: 10.1021/acs.jctc.3c01361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/17/2024]
Abstract
We propose score dynamics (SD), a general framework for learning accelerated evolution operators with large timesteps from molecular dynamics (MD) simulations. SD is centered around scores or derivatives of the transition log-probability with respect to the dynamical degrees of freedom. The latter play the same role as force fields in MD but are used in denoising diffusion probability models to generate discrete transitions of the dynamical variables in an SD time step, which can be orders of magnitude larger than a typical MD time step. In this work, we construct graph neural network-based SD models of realistic molecular systems that are evolved with 10 ps timesteps. We demonstrate the efficacy of SD with case studies of the alanine dipeptide and short alkanes in aqueous solution. Both equilibrium predictions derived from the stationary distributions of the conditional probability and kinetic predictions for the transition rates and transition paths are in good agreement with MD. Our current SD implementation is about 2 orders of magnitude faster than the MD counterpart for the systems studied in this work. Open challenges and possible future remedies to improve SD are also discussed.
Collapse
Affiliation(s)
- Tim Hsu
- Lawrence Livermore National Laboratory, Livermore, California 94551, United States
| | - Babak Sadigh
- Lawrence Livermore National Laboratory, Livermore, California 94551, United States
| | - Vasily Bulatov
- Lawrence Livermore National Laboratory, Livermore, California 94551, United States
| | - Fei Zhou
- Lawrence Livermore National Laboratory, Livermore, California 94551, United States
| |
Collapse
|
42
|
Zhao D, Zhao Y, Xu E, Liu W, Ayers PW, Liu S, Chen D. Fragment-Based Deep Learning for Simultaneous Prediction of Polarizabilities and NMR Shieldings of Macromolecules and Their Aggregates. J Chem Theory Comput 2024; 20:2655-2665. [PMID: 38441881 DOI: 10.1021/acs.jctc.3c01415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/08/2024]
Abstract
Simultaneous prediction of the molecular response properties, such as polarizability and the NMR shielding constant, at a low computational cost is an unresolved issue. We propose to combine a linear-scaling generalized energy-based fragmentation (GEBF) method and deep learning (DL) with both molecular and atomic information-theoretic approach (ITA) quantities as effective descriptors. In GEBF, the total molecular polarizability can be assembled as a linear combination of the corresponding quantities calculated from a set of small embedded subsystems in GEBF. In the new GEBF-DL(ITA) protocol, one can predict subsystem polarizabilities based on the corresponding molecular wave function (thus electron density and ITA quantities) and DL model rather than calculate them from the computationally intensive coupled-perturbed Hartree-Fock or Kohn-Sham equations and finally obtain the total molecular polarizability via a linear combination equation. As a proof-of-concept application, we predict the molecular polarizabilities of large proteins and protein aggregates. GEBF-DL(ITA) is shown to be as accurate enough as GEBF, with mean absolute percentage error <1%. For the largest protein aggregate (>4000 atoms), GEBF-DL(ITA) gains a speedup ratio of 3 compared with GEBF. It is anticipated that when more advanced electronic structure methods are used, this advantage will be more appealing. Moreover, one can also predict the NMR chemical shieldings of proteins with reasonably good accuracy. Overall, the cost-efficient GEBF-DL(ITA) protocol should be a robust theoretical tool for simultaneously predicting polarizabilities and NMR shieldings of large systems.
Collapse
Affiliation(s)
- Dongbo Zhao
- Institute of Biomedical Research, Yunnan University, Kunming, Yunnan 650500, P. R. China
| | - Yilin Zhao
- Department of Chemistry and Chemical Biology, McMaster University, Hamilton ONL8S4M1, Canada
| | - Enhua Xu
- Graduate School of System Informatics, Kobe University, Nada-ku, Kobe, Hyogo 657-8501, Japan
| | - Wenqi Liu
- Institute of Biomedical Research, Yunnan University, Kunming, Yunnan 650500, P. R. China
| | - Paul W Ayers
- Department of Chemistry and Chemical Biology, McMaster University, Hamilton ONL8S4M1, Canada
| | - Shubin Liu
- Research Computing Center, University of North Carolina, Chapel Hill, North Carolina 27599-3420, United States
- Department of Chemistry, University of North Carolina, Chapel Hill, North Carolina 27599-3290, United States
| | - Dahua Chen
- Institute of Biomedical Research, Yunnan University, Kunming, Yunnan 650500, P. R. China
| |
Collapse
|
43
|
Korolev V, Mitrofanov A. Coarse-Grained Crystal Graph Neural Networks for Reticular Materials Design. J Chem Inf Model 2024; 64:1919-1931. [PMID: 38456446 DOI: 10.1021/acs.jcim.3c02083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/09/2024]
Abstract
Reticular materials, including metal-organic frameworks and covalent organic frameworks, combine the relative ease of synthesis and an impressive range of applications in various fields from gas storage to biomedicine. Diverse properties arise from the variation of building units─metal centers and organic linkers─in almost infinite chemical space. Such variation substantially complicates the experimental design and promotes the use of computational methods. In particular, the most successful artificial intelligence algorithms for predicting the properties of reticular materials are atomic-level graph neural networks, which optionally incorporate domain knowledge. Nonetheless, the data-driven inverse design involving these models suffers from the incorporation of irrelevant and redundant features such as a full atomistic graph and network topology. In this study, we propose a new way of representing materials, aiming to overcome the limitations of existing methods; the message passing is performed on a coarse-grained crystal graph that comprises molecular building units. To highlight the merits of our approach, we assessed the predictive performance and energy efficiency of neural networks built on different materials representations, including composition-based and crystal-structure-aware models. Coarse-grained crystal graph neural networks showed decent accuracy at low computational costs, making them a valuable alternative to omnipresent atomic-level algorithms. Moreover, the presented models can be successfully integrated into an inverse materials design pipeline as estimators of the objective function. Overall, the coarse-grained crystal graph framework is aimed at challenging the prevailing atom-centric perspective on reticular materials design.
Collapse
Affiliation(s)
- Vadim Korolev
- Department of Chemistry, Lomonosov Moscow State University, Moscow 119991, Russia
- MSU Institute for Artificial Intelligence, Lomonosov Moscow State University, Moscow 119192, Russia
| | - Artem Mitrofanov
- Department of Chemistry, Lomonosov Moscow State University, Moscow 119991, Russia
- MSU Institute for Artificial Intelligence, Lomonosov Moscow State University, Moscow 119192, Russia
| |
Collapse
|
44
|
Li X, Xiang S, Li G. Application of artificial intelligence in brain arteriovenous malformations: Angioarchitectures, clinical symptoms and prognosis prediction. Interv Neuroradiol 2024:15910199241238798. [PMID: 38515371 DOI: 10.1177/15910199241238798] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/23/2024] Open
Abstract
BACKGROUND Artificial intelligence (AI) has rapidly advanced in the medical field, leveraging its intelligence and automation for the management of various diseases. Brain arteriovenous malformations (AVM) are particularly noteworthy, experiencing rapid development in recent years and yielding remarkable results. This paper aims to summarize the applications of AI in the management of AVMs management. METHODS Literatures published in PubMed during 1999-2022, discussing AI application in AVMs management were reviewed. RESULTS AI algorithms have been applied in various aspects of AVM management, particularly in machine learning and deep learning models. Automatic lesion segmentation or delineation is a promising application that can be further developed and verified. Prognosis prediction using machine learning algorithms with radiomic-based analysis is another meaningful application. CONCLUSIONS AI has been widely used in AVMs management. This article summarizes the current research progress, limitations and future research directions.
Collapse
Affiliation(s)
- Xiangyu Li
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Sishi Xiang
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Guilin Li
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
| |
Collapse
|
45
|
Pracht P, Grimme S, Bannwarth C, Bohle F, Ehlert S, Feldmann G, Gorges J, Müller M, Neudecker T, Plett C, Spicher S, Steinbach P, Wesołowski PA, Zeller F. CREST-A program for the exploration of low-energy molecular chemical space. J Chem Phys 2024; 160:114110. [PMID: 38511658 DOI: 10.1063/5.0197592] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2024] [Accepted: 02/29/2024] [Indexed: 03/22/2024] Open
Abstract
Conformer-rotamer sampling tool (CREST) is an open-source program for the efficient and automated exploration of molecular chemical space. Originally developed in Pracht et al. [Phys. Chem. Chem. Phys. 22, 7169 (2020)] as an automated driver for calculations at the extended tight-binding level (xTB), it offers a variety of molecular- and metadynamics simulations, geometry optimization, and molecular structure analysis capabilities. Implemented algorithms include automated procedures for conformational sampling, explicit solvation studies, the calculation of absolute molecular entropy, and the identification of molecular protonation and deprotonation sites. Calculations are set up to run concurrently, providing efficient single-node parallelization. CREST is designed to require minimal user input and comes with an implementation of the GFNn-xTB Hamiltonians and the GFN-FF force-field. Furthermore, interfaces to any quantum chemistry and force-field software can easily be created. In this article, we present recent developments in the CREST code and show a selection of applications for the most important features of the program. An important novelty is the refactored calculation backend, which provides significant speed-up for sampling of small or medium-sized drug molecules and allows for more sophisticated setups, for example, quantum mechanics/molecular mechanics and minimum energy crossing point calculations.
Collapse
Affiliation(s)
- Philipp Pracht
- Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom
| | - Stefan Grimme
- Mulliken Center for Theoretical Chemistry, Institute for Physical and Theoretical Chemistry, University of Bonn, Beringstr. 4, 53115 Bonn, Germany
| | - Christoph Bannwarth
- Institute for Physical Chemistry, RWTH Aachen University, Melatener Str. 20, 52056 Aachen, Germany
| | - Fabian Bohle
- Mulliken Center for Theoretical Chemistry, Institute for Physical and Theoretical Chemistry, University of Bonn, Beringstr. 4, 53115 Bonn, Germany
| | - Sebastian Ehlert
- AI4Science, Microsoft Research, Evert van de Beekstraat 354, 1118 CZ Schiphol, The Netherlands
| | - Gereon Feldmann
- Institute for Physical Chemistry, RWTH Aachen University, Melatener Str. 20, 52056 Aachen, Germany
| | - Johannes Gorges
- Mulliken Center for Theoretical Chemistry, Institute for Physical and Theoretical Chemistry, University of Bonn, Beringstr. 4, 53115 Bonn, Germany
| | - Marcel Müller
- Mulliken Center for Theoretical Chemistry, Institute for Physical and Theoretical Chemistry, University of Bonn, Beringstr. 4, 53115 Bonn, Germany
| | - Tim Neudecker
- Institute for Physical and Theoretical Chemistry, University of Bremen, 28359 Bremen, Germany
| | - Christoph Plett
- Mulliken Center for Theoretical Chemistry, Institute for Physical and Theoretical Chemistry, University of Bonn, Beringstr. 4, 53115 Bonn, Germany
| | | | - Pit Steinbach
- Institute for Physical Chemistry, RWTH Aachen University, Melatener Str. 20, 52056 Aachen, Germany
| | - Patryk A Wesołowski
- Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom
| | - Felix Zeller
- Institute for Physical and Theoretical Chemistry, University of Bremen, 28359 Bremen, Germany
| |
Collapse
|
46
|
Stefanou AD, Zianni X. Physics mechanisms underlying the optimization of coherent heat transfer across width-modulated nanowaveguides with calculations and machine learning. JOURNAL OF PHYSICS. CONDENSED MATTER : AN INSTITUTE OF PHYSICS JOURNAL 2024; 36:245301. [PMID: 38457837 DOI: 10.1088/1361-648x/ad31c0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Accepted: 03/08/2024] [Indexed: 03/10/2024]
Abstract
Optimization of heat transfer at the nanoscale is necessary for efficient modern technology applications in nanoelectronics, energy conversion, and quantum technologies. In such applications, phonons dominate thermal transport and optimal performance requires minimum phonon conduction. Coherent phonon conduction is minimized by maximum disorder in the aperiodic modulation profile of width-modulated nanowaveguides, according to a physics rule. It is minimized for moderate disorder against physics intuition in composite nanostructures. Such counter behaviors call for a better understanding of the optimization of phonon transport in non-uniform nanostructures. We have explored mechanisms underlying the optimization of width-modulated nanowaveguides with calculations and machine learning, and we report on generic behavior. We show that the distribution of the thermal conductance among the aperiodic width-modulation configurations is controlled by the modulation degree irrespective of choices of constituent material, width-modulation-geometry, and composition constraints. The efficiency of Bayesian optimization is evaluated against increasing temperature and sample size. It is found that it decreases with increasing temperature due to thermal broadening of the thermal conductance distribution. It shows weak dependence on temperature in samples with high discreteness in the distribution spectrum. Our work provides new physics insight and indicates research pathways to optimize heat transfer in non-uniform nanostructures.
Collapse
Affiliation(s)
- Antonios-Dimitrios Stefanou
- Department of Aerospace Science and Technology, School of Science, National and Kapodistrian University of Athens, 34 400 Psachna, Greece
| | - Xanthippi Zianni
- Department of Aerospace Science and Technology, School of Science, National and Kapodistrian University of Athens, 34 400 Psachna, Greece
| |
Collapse
|
47
|
Dral PO. AI in computational chemistry through the lens of a decade-long journey. Chem Commun (Camb) 2024; 60:3240-3258. [PMID: 38444290 DOI: 10.1039/d4cc00010b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/07/2024]
Abstract
This article gives a perspective on the progress of AI tools in computational chemistry through the lens of the author's decade-long contributions put in the wider context of the trends in this rapidly expanding field. This progress over the last decade is tremendous: while a decade ago we had a glimpse of what was to come through many proof-of-concept studies, now we witness the emergence of many AI-based computational chemistry tools that are mature enough to make faster and more accurate simulations increasingly routine. Such simulations in turn allow us to validate and even revise experimental results, deepen our understanding of the physicochemical processes in nature, and design better materials, devices, and drugs. The rapid introduction of powerful AI tools gives rise to unique challenges and opportunities that are discussed in this article too.
Collapse
Affiliation(s)
- Pavlo O Dral
- State Key Laboratory of Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, and Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen, Fujian 361005, China.
| |
Collapse
|
48
|
Miao L, Jia W, Cao X, Jiao L. Computational chemistry for water-splitting electrocatalysis. Chem Soc Rev 2024; 53:2771-2807. [PMID: 38344774 DOI: 10.1039/d2cs01068b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
Electrocatalytic water splitting driven by renewable electricity has attracted great interest in recent years for producing hydrogen with high-purity. However, the practical applications of this technology are limited by the development of electrocatalysts with high activity, low cost, and long durability. In the search for new electrocatalysts, computational chemistry has made outstanding contributions by providing fundamental laws that govern the electron behavior and enabling predictions of electrocatalyst performance. This review delves into theoretical studies on electrochemical water-splitting processes. Firstly, we introduce the fundamentals of electrochemical water electrolysis and subsequently discuss the current advancements in computational methods and models for electrocatalytic water splitting. Additionally, a comprehensive overview of benchmark descriptors is provided to aid in understanding intrinsic catalytic performance for water-splitting electrocatalysts. Finally, we critically evaluate the remaining challenges within this field.
Collapse
Affiliation(s)
- Licheng Miao
- Key Laboratory of Advanced Energy Materials Chemistry (Ministry of Education), Collaborative Innovation Center of Chemical Science and Engineering (Tianjin), College of Chemistry, Nankai University, Tianjin 300071, China.
| | - Wenqi Jia
- Key Laboratory of Advanced Energy Materials Chemistry (Ministry of Education), Collaborative Innovation Center of Chemical Science and Engineering (Tianjin), College of Chemistry, Nankai University, Tianjin 300071, China.
| | - Xuejie Cao
- Key Laboratory of Advanced Energy Materials Chemistry (Ministry of Education), Collaborative Innovation Center of Chemical Science and Engineering (Tianjin), College of Chemistry, Nankai University, Tianjin 300071, China.
| | - Lifang Jiao
- Key Laboratory of Advanced Energy Materials Chemistry (Ministry of Education), Collaborative Innovation Center of Chemical Science and Engineering (Tianjin), College of Chemistry, Nankai University, Tianjin 300071, China.
| |
Collapse
|
49
|
Akbar B, Tayara H, Chong KT. Unveiling dominant recombination loss in perovskite solar cells with a XGBoost-based machine learning approach. iScience 2024; 27:109200. [PMID: 38420582 PMCID: PMC10901077 DOI: 10.1016/j.isci.2024.109200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 12/12/2023] [Accepted: 02/07/2024] [Indexed: 03/02/2024] Open
Abstract
Remarkable and intelligent perovskite solar cells (PSCs) have attracted substantial attention from researchers and are undergoing rapid advancements in photovoltaic technology. These developments aim to create highly efficient energy devices with fewer dominant recombination losses within the realm of third-generation solar cells. Diverse machine learning (ML) algorithms implemented, addressing dominant losses due to recombination in PSCs, focusing on grain boundaries (GBs), interfaces, and band-to-band recombination. The extreme gradient boosting (XGBoost) classifier effectively predicts the recombination losses. Our model trained with 7-fold cross-validation to ensure generalizability and robustness. Leveraging Optuna and shapley additive explanations (SHAP) for hyperparameter optimization and investigate the influence of features on target variables, achieved 85% accuracy on over 2 million simulated data, respectively. Because of the input parameters (light intensity and open-circuit voltage), the performance evaluation measures for the dominant losses caused by the recombination predicted by proposed model were superior to those of state-of-the-art models.
Collapse
Affiliation(s)
- Basir Akbar
- Graduate School of Integrated Energy-AI, Jeonbuk National University, Jeonju 54896, South Korea
| | - Hilal Tayara
- School of International Engineering and Science, Jeonbuk National University, Jeonju 54896, South Korea
| | - Kil To Chong
- Advances Electronics and Information Research Center, Jeonbuk National University, Jeonju 54896, South Korea
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, South Korea
| |
Collapse
|
50
|
Yu L, Zhang W, Nie Z, Duan J, Chen S. Machine learning guided tuning charge distribution by composition in MOFs for oxygen evolution reaction. RSC Adv 2024; 14:9032-9037. [PMID: 38500624 PMCID: PMC10945371 DOI: 10.1039/d3ra08873a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Accepted: 02/25/2024] [Indexed: 03/20/2024] Open
Abstract
Traditional design/optimization of metal-organic frameworks (MOFs) is time-consuming and labor-intensive. In this study, we utilize machine learning (ML) to accelerate the synthesis of MOFs. We have built a library of over 900 MOFs with different metal salts, solvent ratios, reaction durations and temperatures, and utilize zeta potentials as target variables for ML training. A total of four ML models have been used to train the collected dataset and assess their convergence performances, where Random Forest Regression (RFR) and Gradient Boosting Regression (GBR) models show strong correlation and accurate predictions. We then predicted two kinds of MOFs from RFR and GBR models. Remarkably, the experimentally data of the synthesized MOFs closely matched the predicted results, and these MOFs exhibited excellent electrocatalytic performances for oxygen evolution. This study would have general implications in the utilization of machine learning for accelerating the synthesis of MOFs for diverse applications.
Collapse
Affiliation(s)
- Licheng Yu
- Key Laboratory for Soft Chemistry and Functional Materials (Ministry of Education), School of Chemistry and Chemical Engineering, School of Energy and Power Engineering, Nanjing University of Science and Technology Nanjing 210094 China
| | - Wenwen Zhang
- Key Laboratory for Soft Chemistry and Functional Materials (Ministry of Education), School of Chemistry and Chemical Engineering, School of Energy and Power Engineering, Nanjing University of Science and Technology Nanjing 210094 China
| | - Zhihao Nie
- Key Laboratory for Soft Chemistry and Functional Materials (Ministry of Education), School of Chemistry and Chemical Engineering, School of Energy and Power Engineering, Nanjing University of Science and Technology Nanjing 210094 China
| | - Jingjing Duan
- Key Laboratory for Soft Chemistry and Functional Materials (Ministry of Education), School of Chemistry and Chemical Engineering, School of Energy and Power Engineering, Nanjing University of Science and Technology Nanjing 210094 China
| | - Sheng Chen
- Key Laboratory for Soft Chemistry and Functional Materials (Ministry of Education), School of Chemistry and Chemical Engineering, School of Energy and Power Engineering, Nanjing University of Science and Technology Nanjing 210094 China
| |
Collapse
|