1
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Kalayan J, Ramzan I, Williams CD, Bryce RA, Burton NA. A neural network potential based on pairwise resolved atomic forces and energies. J Comput Chem 2024; 45:1143-1151. [PMID: 38284556 DOI: 10.1002/jcc.27313] [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: 10/31/2023] [Revised: 12/23/2023] [Accepted: 01/05/2024] [Indexed: 01/30/2024]
Abstract
Molecular simulations have become a key tool in molecular and materials design. Machine learning (ML)-based potential energy functions offer the prospect of simulating complex molecular systems efficiently at quantum chemical accuracy. In previous work, we have introduced the ML-based PairF-Net approach to neural network potentials, that adopts a pairwise interatomic scheme to predicting forces within a molecular system. Here, we further develop the PairF-Net model to intrinsically incorporate energy conservation and couple the model to a molecular mechanical (MM) environment within the OpenMM package. The updated PairF-Net model yields energy and force predictions and dynamical distributions in good agreement with the rMD17 dataset of ten small organic molecules in the gas-phase. We further show that these in vacuo ML models of small molecules can be applied to force predictions in aqueous solution via hybrid ML/MM simulations. We present a new benchmark dataset for these ten molecules in solution, obtained from QM/MM simulations, which we denote as rMD17-aq (https://zenodo.org/records/10048644); and assess the ability of PairF-Net to reproduce the molecular energy, atomic forces and dynamical distributions of these solution conformations via ML/MM simulations.
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Affiliation(s)
- Jas Kalayan
- Division of Pharmacy and Optometry, School of Health Sciences, University of Manchester, Manchester, UK
| | - Ismaeel Ramzan
- Division of Pharmacy and Optometry, School of Health Sciences, University of Manchester, Manchester, UK
- Neural Circuits and Computations Unit, RIKEN Center for Brain Science, Wako, Japan
| | - Christopher D Williams
- Division of Pharmacy and Optometry, School of Health Sciences, University of Manchester, Manchester, UK
| | - Richard A Bryce
- Division of Pharmacy and Optometry, School of Health Sciences, University of Manchester, Manchester, UK
| | - Neil A Burton
- Department of Chemistry, University of Manchester, Manchester, UK
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2
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Zinovjev K, Hedges L, Montagud Andreu R, Woods C, Tuñón I, van der Kamp MW. emle-engine: A Flexible Electrostatic Machine Learning Embedding Package for Multiscale Molecular Dynamics Simulations. J Chem Theory Comput 2024. [PMID: 38804055 DOI: 10.1021/acs.jctc.4c00248] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
We present in this work the emle-engine package (https://github.com/chemle/emle-engine)─the implementation of a new machine learning embedding scheme for hybrid machine learning potential/molecular-mechanics (ML/MM) dynamics simulations. The package is based on an embedding scheme that uses a physics-based model of the electronic density and induction with a handful of tunable parameters derived from in vacuo properties of the subsystem to be embedded. This scheme is completely independent of the in vacuo potential and requires only the positions of the atoms of the machine learning subsystem and the positions and partial charges of the molecular mechanics environment. These characteristics allow emle-engine to be employed in existing QM/MM software. We demonstrate that the implemented electrostatic machine learning embedding scheme (named EMLE) is stable in enhanced sampling molecular dynamics simulations. Through the calculation of free energy surfaces of alanine dipeptide in water with two different ML options for the in vacuo potential and three embedding models, we test the performance of EMLE. When compared to the reference DFT/MM surface, the EMLE embedding is clearly superior to the MM one based on fixed partial charges. The configurational dependence of the electronic density and the inclusion of the induction energy introduced by the EMLE model leads to a systematic reduction in the average error of the free energy surface when compared to MM embedding. By enabling the usage of EMLE embedding in practical ML/MM simulations, emle-engine will make it possible to accurately model systems and processes that feature significant variations in the charge distribution of the ML subsystem and/or the interacting environment.
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Affiliation(s)
- Kirill Zinovjev
- Departamento de Química Física, Universidad de Valencia, 46100 Burjassot, Spain
| | - Lester Hedges
- School of Biochemistry, University of Bristol, Biomedical Sciences Building, University Walk, Bristol BS8 1TD, U.K
- Research Software Engineering, Advanced Computing Research Centre, 31 Great George Street, Bristol BS1 5QD, U.K
| | | | - Christopher Woods
- Research Software Engineering, Advanced Computing Research Centre, 31 Great George Street, Bristol BS1 5QD, U.K
| | - Iñaki Tuñón
- Departamento de Química Física, Universidad de Valencia, 46100 Burjassot, Spain
| | - Marc W van der Kamp
- School of Biochemistry, University of Bristol, Biomedical Sciences Building, University Walk, Bristol BS8 1TD, U.K
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3
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Aldossary A, Campos-Gonzalez-Angulo JA, Pablo-García S, Leong SX, Rajaonson EM, Thiede L, Tom G, Wang A, Avagliano D, Aspuru-Guzik A. In Silico Chemical Experiments in the Age of AI: From Quantum Chemistry to Machine Learning and Back. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024:e2402369. [PMID: 38794859 DOI: 10.1002/adma.202402369] [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: 04/28/2024] [Indexed: 05/26/2024]
Abstract
Computational chemistry is an indispensable tool for understanding molecules and predicting chemical properties. However, traditional computational methods face significant challenges due to the difficulty of solving the Schrödinger equations and the increasing computational cost with the size of the molecular system. In response, there has been a surge of interest in leveraging artificial intelligence (AI) and machine learning (ML) techniques to in silico experiments. Integrating AI and ML into computational chemistry increases the scalability and speed of the exploration of chemical space. However, challenges remain, particularly regarding the reproducibility and transferability of ML models. This review highlights the evolution of ML in learning from, complementing, or replacing traditional computational chemistry for energy and property predictions. Starting from models trained entirely on numerical data, a journey set forth toward the ideal model incorporating or learning the physical laws of quantum mechanics. This paper also reviews existing computational methods and ML models and their intertwining, outlines a roadmap for future research, and identifies areas for improvement and innovation. Ultimately, the goal is to develop AI architectures capable of predicting accurate and transferable solutions to the Schrödinger equation, thereby revolutionizing in silico experiments within chemistry and materials science.
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Affiliation(s)
- Abdulrahman Aldossary
- Department of Chemistry, University of Toronto, 80 St. George Street, Toronto, ON, M5S 3H6, Canada
| | | | - Sergio Pablo-García
- Department of Chemistry, University of Toronto, 80 St. George Street, Toronto, ON, M5S 3H6, Canada
- Department of Computer Science, University of Toronto, 40 St. George Street, Toronto, ON, M5S 2E4, Canada
| | - Shi Xuan Leong
- Department of Chemistry, University of Toronto, 80 St. George Street, Toronto, ON, M5S 3H6, Canada
| | - Ella Miray Rajaonson
- Department of Chemistry, University of Toronto, 80 St. George Street, Toronto, ON, M5S 3H6, Canada
- Vector Institute for Artificial Intelligence, 661 University Ave. Suite 710, Toronto, ON, M5G 1M1, Canada
| | - Luca Thiede
- Department of Computer Science, University of Toronto, 40 St. George Street, Toronto, ON, M5S 2E4, Canada
- Vector Institute for Artificial Intelligence, 661 University Ave. Suite 710, Toronto, ON, M5G 1M1, Canada
| | - Gary Tom
- Department of Chemistry, University of Toronto, 80 St. George Street, Toronto, ON, M5S 3H6, Canada
- Vector Institute for Artificial Intelligence, 661 University Ave. Suite 710, Toronto, ON, M5G 1M1, Canada
| | - Andrew Wang
- Department of Chemistry, University of Toronto, 80 St. George Street, Toronto, ON, M5S 3H6, Canada
| | - Davide Avagliano
- Chimie ParisTech, PSL University, CNRS, Institute of Chemistry for Life and Health Sciences (iCLeHS UMR 8060), Paris, F-75005, France
| | - Alán Aspuru-Guzik
- Department of Chemistry, University of Toronto, 80 St. George Street, Toronto, ON, M5S 3H6, Canada
- Department of Computer Science, University of Toronto, 40 St. George Street, Toronto, ON, M5S 2E4, Canada
- Vector Institute for Artificial Intelligence, 661 University Ave. Suite 710, Toronto, ON, M5G 1M1, Canada
- Department of Materials Science & Engineering, University of Toronto, 184 College St., Toronto, ON, M5S 3E4, Canada
- Department of Chemical Engineering & Applied Chemistry, University of Toronto, 200 College St., Toronto, ON, M5S 3E5, Canada
- Lebovic Fellow, Canadian Institute for Advanced Research (CIFAR), 66118 University Ave., Toronto, M5G 1M1, Canada
- Acceleration Consortium, 80 St George St, Toronto, M5S 3H6, Canada
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4
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Choyal V, Sagar N, Sai Gautam G. Constructing and Evaluating Machine-Learned Interatomic Potentials for Li-Based Disordered Rocksalts. J Chem Theory Comput 2024. [PMID: 38787289 DOI: 10.1021/acs.jctc.4c00039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/25/2024]
Abstract
Lithium-based disordered rocksalts (LDRs), which are an important class of positive electrode materials that can increase the energy density of current Li-ion batteries, represent a significantly complex chemical and configurational space for conventional density functional theory (DFT)-based high-throughput screening approaches. Notably, atom-centered machine-learned interatomic potentials (MLIPs) are a promising pathway to accurately model the potential energy surface of highly disordered chemical spaces, such as LDRs, where the performance of such MLIPs has not been rigorously explored yet. Here, we represent a comprehensive evaluation of the accuracy, transferability, and ease of training of five atom-centered MLIPs, including the artificial neural network potentials developed by the atomic energy network (AENET), the Gaussian approximation potential (GAP), the spectral neighbor analysis potential (SNAP) and its quadratic extension (qSNAP), and the moment tensor potential (MTP), in modeling a 11-component LDR chemical space. Specifically, we generate a DFT-calculated data set of 10,842 configurations of disordered LiTMO2 and TMO2 compositions, where TM = Sc, Ti, V, Cr, Mn, Fe, Co, Ni, and/or Cu. To provide a point-of-comparison on the performance of atom-centered MLIPs, we also trained the neural equivariant interatomic potential (NequIP) on a subset of our data. Importantly, we find AENET to be the best potential in terms of accuracy and transferability for energy predictions, while MTP is the best for atomic forces. While AENET is the fastest to train among the MLIPs considered at low number of epochs (300), the training time increases significantly as epochs increase (3300), with a corresponding reduction in training errors (∼60%). Note that AENET and GAP tend to overfit in small data sets, with the extent of overfitting reducing with larger data sets. Finally, we observe AENET to provide reasonable predictions of average Li-intercalation voltages in layered, single-TM LiTMO2 frameworks, compared to DFT (∼10% error on average). Our study should pave the way both for discovering novel disordered rocksalt electrodes and for modeling other configurationally complex systems, such as high-entropy ceramics and alloys.
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Affiliation(s)
- Vijay Choyal
- Department of Materials Engineering, Indian Institute of Science, Bengaluru 560012, Karnataka, India
| | - Nidhish Sagar
- Department of Materials Engineering, Indian Institute of Science, Bengaluru 560012, Karnataka, India
| | - Gopalakrishnan Sai Gautam
- Department of Materials Engineering, Indian Institute of Science, Bengaluru 560012, Karnataka, India
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5
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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.
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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
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6
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Duignan TT. The Potential of Neural Network Potentials. ACS PHYSICAL CHEMISTRY AU 2024; 4:232-241. [PMID: 38800721 PMCID: PMC11117678 DOI: 10.1021/acsphyschemau.4c00004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Revised: 03/04/2024] [Accepted: 03/05/2024] [Indexed: 05/29/2024]
Abstract
In the next half-century, physical chemistry will likely undergo a profound transformation, driven predominantly by the combination of recent advances in quantum chemistry and machine learning (ML). Specifically, equivariant neural network potentials (NNPs) are a breakthrough new tool that are already enabling us to simulate systems at the molecular scale with unprecedented accuracy and speed, relying on nothing but fundamental physical laws. The continued development of this approach will realize Paul Dirac's 80-year-old vision of using quantum mechanics to unify physics with chemistry and providing invaluable tools for understanding materials science, biology, earth sciences, and beyond. The era of highly accurate and efficient first-principles molecular simulations will provide a wealth of training data that can be used to build automated computational methodologies, using tools such as diffusion models, for the design and optimization of systems at the molecular scale. Large language models (LLMs) will also evolve into increasingly indispensable tools for literature review, coding, idea generation, and scientific writing.
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7
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Capone M, Romanelli M, Castaldo D, Parolin G, Bello A, Gil G, Vanzan M. A Vision for the Future of Multiscale Modeling. ACS PHYSICAL CHEMISTRY AU 2024; 4:202-225. [PMID: 38800726 PMCID: PMC11117712 DOI: 10.1021/acsphyschemau.3c00080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/30/2023] [Revised: 01/31/2024] [Accepted: 02/01/2024] [Indexed: 05/29/2024]
Abstract
The rise of modern computer science enabled physical chemistry to make enormous progresses in understanding and harnessing natural and artificial phenomena. Nevertheless, despite the advances achieved over past decades, computational resources are still insufficient to thoroughly simulate extended systems from first principles. Indeed, countless biological, catalytic and photophysical processes require ab initio treatments to be properly described, but the breadth of length and time scales involved makes it practically unfeasible. A way to address these issues is to couple theories and algorithms working at different scales by dividing the system into domains treated at different levels of approximation, ranging from quantum mechanics to classical molecular dynamics, even including continuum electrodynamics. This approach is known as multiscale modeling and its use over the past 60 years has led to remarkable results. Considering the rapid advances in theory, algorithm design, and computing power, we believe multiscale modeling will massively grow into a dominant research methodology in the forthcoming years. Hereby we describe the main approaches developed within its realm, highlighting their achievements and current drawbacks, eventually proposing a plausible direction for future developments considering also the emergence of new computational techniques such as machine learning and quantum computing. We then discuss how advanced multiscale modeling methods could be exploited to address critical scientific challenges, focusing on the simulation of complex light-harvesting processes, such as natural photosynthesis. While doing so, we suggest a cutting-edge computational paradigm consisting in performing simultaneous multiscale calculations on a system allowing the various domains, treated with appropriate accuracy, to move and extend while they properly interact with each other. Although this vision is very ambitious, we believe the quick development of computer science will lead to both massive improvements and widespread use of these techniques, resulting in enormous progresses in physical chemistry and, eventually, in our society.
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Affiliation(s)
- Matteo Capone
- Department
of Physical and Chemical Sciences, University
of L’Aquila, L’Aquila 67010, Italy
| | - Marco Romanelli
- Department
of Chemical Sciences, University of Padova, Padova 35131, Italy
| | - Davide Castaldo
- Department
of Chemical Sciences, University of Padova, Padova 35131, Italy
| | - Giovanni Parolin
- Department
of Chemical Sciences, University of Padova, Padova 35131, Italy
| | - Alessandro Bello
- Department
of Chemical Sciences, University of Padova, Padova 35131, Italy
- Department
of Physics, Informatics and Mathematics, University of Modena and Reggio Emilia, Modena 41125, Italy
| | - Gabriel Gil
- Department
of Chemical Sciences, University of Padova, Padova 35131, Italy
- Instituto
de Cibernética, Matemática y Física (ICIMAF), La Habana 10400, Cuba
| | - Mirko Vanzan
- Department
of Chemical Sciences, University of Padova, Padova 35131, Italy
- Department
of Physics, University of Milano, Milano 20133, Italy
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8
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Wang G, Wang C, Zhang X, Li Z, Zhou J, Sun Z. Machine learning interatomic potential: Bridge the gap between small-scale models and realistic device-scale simulations. iScience 2024; 27:109673. [PMID: 38646181 PMCID: PMC11033164 DOI: 10.1016/j.isci.2024.109673] [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] [Indexed: 04/23/2024] Open
Abstract
Machine learning interatomic potential (MLIP) overcomes the challenges of high computational costs in density-functional theory and the relatively low accuracy in classical large-scale molecular dynamics, facilitating more efficient and precise simulations in materials research and design. In this review, the current state of the four essential stages of MLIP is discussed, including data generation methods, material structure descriptors, six unique machine learning algorithms, and available software. Furthermore, the applications of MLIP in various fields are investigated, notably in phase-change memory materials, structure searching, material properties predicting, and the pre-trained universal models. Eventually, the future perspectives, consisting of standard datasets, transferability, generalization, and trade-off between accuracy and complexity in MLIPs, are reported.
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Affiliation(s)
- Guanjie Wang
- School of Materials Science and Engineering, Beihang University, Beijing 100191, China
- School of Integrated Circuit Science and Engineering, Beihang University, Beijing 100191, China
| | - Changrui Wang
- School of Materials Science and Engineering, Beihang University, Beijing 100191, China
| | - Xuanguang Zhang
- School of Materials Science and Engineering, Beihang University, Beijing 100191, China
| | - Zefeng Li
- School of Materials Science and Engineering, Beihang University, Beijing 100191, China
| | - Jian Zhou
- School of Materials Science and Engineering, Beihang University, Beijing 100191, China
| | - Zhimei Sun
- School of Materials Science and Engineering, Beihang University, Beijing 100191, China
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9
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Fang W, Zhu YC, Cheng Y, Hao YP, Richardson JO. Robust Gaussian Process Regression Method for Efficient Tunneling Pathway Optimization: Application to Surface Processes. J Chem Theory Comput 2024; 20:3766-3778. [PMID: 38708859 PMCID: PMC11099967 DOI: 10.1021/acs.jctc.4c00158] [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: 04/17/2024] [Accepted: 04/17/2024] [Indexed: 05/07/2024]
Abstract
Simulation of surface processes is a key part of computational chemistry that offers atomic-scale insights into mechanisms of heterogeneous catalysis, diffusion dynamics, and quantum tunneling phenomena. The most common theoretical approaches involve optimization of reaction pathways, including semiclassical tunneling pathways (called instantons). The computational effort can be demanding, especially for instanton optimizations with an ab initio electronic structure. Recently, machine learning has been applied to accelerate reaction-pathway optimization, showing great potential for a wide range of applications. However, previous methods still suffer from numerical and efficiency issues and were not designed for condensed-phase reactions. We propose an improved framework based on Gaussian process regression for general transformed coordinates, which has improved efficiency and numerical stability, and we propose a descriptor that combines internal and Cartesian coordinates suitable for modeling surface processes. We demonstrate with 11 instanton optimizations in three representative systems that the improved approach makes ab initio instanton optimization significantly cheaper, such that it becomes not much more expensive than a classical transition-state theory rate calculation.
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Affiliation(s)
- Wei Fang
- Department
of Chemistry, Shanghai Key Laboratory of Molecular Catalysis and Innovative
Materials, Fudan University, Shanghai 200438, P. R. China
- Laboratory
of Physical Chemistry, ETH Zürich, Zürich 8093, Switzerland
- State
Key Laboratory of Molecular Reaction Dynamics and Center for Theoretical
Computational Chemistry, Dalian Institute
of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, P. R. China
| | - Yu-Cheng Zhu
- State
Key Laboratory for Artificial Microstructure and Mesoscopic Physics,
Frontier Science Center for Nano-optoelectronics and School of Physics, Peking University, Beijing 100871, China
| | - Yihan Cheng
- State
Key Laboratory for Artificial Microstructure and Mesoscopic Physics,
Frontier Science Center for Nano-optoelectronics and School of Physics, Peking University, Beijing 100871, China
| | - Yi-Ping Hao
- State
Key Laboratory of Molecular Reaction Dynamics and Center for Theoretical
Computational Chemistry, Dalian Institute
of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, P. R. China
| | - Jeremy O. Richardson
- Department
of Chemistry and Applied Biosciences, ETH
Zürich, Zürich 8093, Switzerland
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10
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Wan K, He J, Shi X. Construction of High Accuracy Machine Learning Interatomic Potential for Surface/Interface of Nanomaterials-A Review. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2305758. [PMID: 37640376 DOI: 10.1002/adma.202305758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 08/24/2023] [Indexed: 08/31/2023]
Abstract
The inherent discontinuity and unique dimensional attributes of nanomaterial surfaces and interfaces bestow them with various exceptional properties. These properties, however, also introduce difficulties for both experimental and computational studies. The advent of machine learning interatomic potential (MLIP) addresses some of the limitations associated with empirical force fields, presenting a valuable avenue for accurate simulations of these surfaces/interfaces of nanomaterials. Central to this approach is the idea of capturing the relationship between system configuration and potential energy, leveraging the proficiency of machine learning (ML) to precisely approximate high-dimensional functions. This review offers an in-depth examination of MLIP principles and their execution and elaborates on their applications in the realm of nanomaterial surface and interface systems. The prevailing challenges faced by this potent methodology are also discussed.
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Affiliation(s)
- Kaiwei Wan
- Laboratory of Theoretical and Computational Nanoscience, National Center for Nanoscience and Technology, Chinese Academy of Sciences, Beijing, 100190, China
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, 100049, China
| | - Jianxin He
- Laboratory of Theoretical and Computational Nanoscience, National Center for Nanoscience and Technology, Chinese Academy of Sciences, Beijing, 100190, China
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, 100049, China
| | - Xinghua Shi
- Laboratory of Theoretical and Computational Nanoscience, National Center for Nanoscience and Technology, Chinese Academy of Sciences, Beijing, 100190, China
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, 100049, China
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11
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Chen M, Jiang X, Zhang L, Chen X, Wen Y, Gu Z, Li X, Zheng M. The emergence of machine learning force fields in drug design. Med Res Rev 2024; 44:1147-1182. [PMID: 38173298 DOI: 10.1002/med.22008] [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: 08/19/2023] [Revised: 11/29/2023] [Accepted: 12/05/2023] [Indexed: 01/05/2024]
Abstract
In the field of molecular simulation for drug design, traditional molecular mechanic force fields and quantum chemical theories have been instrumental but limited in terms of scalability and computational efficiency. To overcome these limitations, machine learning force fields (MLFFs) have emerged as a powerful tool capable of balancing accuracy with efficiency. MLFFs rely on the relationship between molecular structures and potential energy, bypassing the need for a preconceived notion of interaction representations. Their accuracy depends on the machine learning models used, and the quality and volume of training data sets. With recent advances in equivariant neural networks and high-quality datasets, MLFFs have significantly improved their performance. This review explores MLFFs, emphasizing their potential in drug design. It elucidates MLFF principles, provides development and validation guidelines, and highlights successful MLFF implementations. It also addresses potential challenges in developing and applying MLFFs. The review concludes by illuminating the path ahead for MLFFs, outlining the challenges to be overcome and the opportunities to be harnessed. This inspires researchers to embrace MLFFs in their investigations as a new tool to perform molecular simulations in drug design.
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Affiliation(s)
- Mingan Chen
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- School of Physical Science and Technology, ShanghaiTech University, Shanghai, China
- Lingang Laboratory, Shanghai, China
| | - Xinyu Jiang
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- School of Pharmacy, University of Chinese Academy of Sciences, Beijing, China
| | - Lehan Zhang
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- School of Pharmacy, University of Chinese Academy of Sciences, Beijing, China
| | - Xiaoxu Chen
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- School of Pharmacy, University of Chinese Academy of Sciences, Beijing, China
- School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, UCAS, Hangzhou, China
| | - Yiming Wen
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- School of Pharmacy, University of Chinese Academy of Sciences, Beijing, China
- School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, UCAS, Hangzhou, China
| | - Zhiyong Gu
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- School of Pharmacy, University of Chinese Academy of Sciences, Beijing, China
- School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, UCAS, Hangzhou, China
| | - Xutong Li
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- School of Pharmacy, University of Chinese Academy of Sciences, Beijing, China
| | - Mingyue Zheng
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- School of Pharmacy, University of Chinese Academy of Sciences, Beijing, China
- School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, UCAS, Hangzhou, China
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12
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Ge F, Wang R, Qu C, Zheng P, Nandi A, Conte R, Houston PL, Bowman JM, Dral PO. Tell Machine Learning Potentials What They Are Needed For: Simulation-Oriented Training Exemplified for Glycine. J Phys Chem Lett 2024; 15:4451-4460. [PMID: 38626460 DOI: 10.1021/acs.jpclett.4c00746] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/18/2024]
Abstract
Machine learning potentials (MLPs) are widely applied as an efficient alternative way to represent potential energy surfaces (PESs) in many chemical simulations. The MLPs are often evaluated with the root-mean-square errors on the test set drawn from the same distribution as the training data. Here, we systematically investigate the relationship between such test errors and the simulation accuracy with MLPs on an example of a full-dimensional, global PES for the glycine amino acid. Our results show that the errors in the test set do not unambiguously reflect the MLP performance in different simulation tasks, such as relative conformer energies, barriers, vibrational levels, and zero-point vibrational energies. We also offer an easily accessible solution for improving the MLP quality in a simulation-oriented manner, yielding the most precise relative conformer energies and barriers. This solution also passed the stringent test by diffusion Monte Carlo simulations.
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Affiliation(s)
- Fuchun Ge
- 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
| | - Ran Wang
- 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
| | - Chen Qu
- Independent Researcher, Toronto, Ontario M9B0E3, Canada
| | - Peikun Zheng
- 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
| | - Apurba Nandi
- Department of Chemistry and Cherry L. Emerson Center for Scientific Computation, Emory University, Atlanta, Georgia 30322, United States
- Department of Physics and Materials Science, University of Luxembourg, Luxembourg City L-1511, Luxembourg
| | - Riccardo Conte
- Dipartimento di Chimica, Università degli Studi di Milano, via Golgi 19, 20133 Milano, Italy
| | - Paul L Houston
- Department of Chemistry and Chemical Biology, Cornell University, Ithaca, New York 14853, United States
| | - Joel M Bowman
- Department of Chemistry and Cherry L. Emerson Center for Scientific Computation, Emory University, Atlanta, Georgia 30322, United States
| | - 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
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13
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Zhai Y, Rashmi R, Palos E, Paesani F. Many-body interactions and deep neural network potentials for water. J Chem Phys 2024; 160:144501. [PMID: 38587225 DOI: 10.1063/5.0203682] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Accepted: 03/23/2024] [Indexed: 04/09/2024] Open
Abstract
We present a detailed assessment of deep neural network potentials developed within the Deep Potential Molecular Dynamics (DeePMD) framework and trained on the MB-pol data-driven many-body potential energy function. Specific focus is directed at the ability of DeePMD-based potentials to correctly reproduce the accuracy of MB-pol across various water systems. Analyses of bulk and interfacial properties as well as many-body interactions characteristic of water elucidate inherent limitations in the transferability and predictive accuracy of DeePMD-based potentials. These limitations can be traced back to an incomplete implementation of the "nearsightedness of electronic matter" principle, which may be common throughout machine learning potentials that do not include a proper representation of self-consistently determined long-range electric fields. These findings provide further support for the "short-blanket dilemma" faced by DeePMD-based potentials, highlighting the challenges in achieving a balance between computational efficiency and a rigorous, physics-based representation of the properties of water. Finally, we believe that our study contributes to the ongoing discourse on the development and application of machine learning models in simulating water systems, offering insights that could guide future improvements in the field.
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Affiliation(s)
- Yaoguang Zhai
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California 92093, USA
- Department of Computer Science and Engineering, University of California San Diego, La Jolla, California 92093, USA
| | - Richa Rashmi
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California 92093, USA
| | - Etienne Palos
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California 92093, USA
| | - Francesco Paesani
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California 92093, USA
- Materials Science and Engineering, University of California San Diego, La Jolla, California 92093, USA
- Halicioğlu Data Science Institute, University of California San Diego, La Jolla, California 92093, USA
- San Diego Supercomputer Center, University of California San Diego, La Jolla, California 92093, USA
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14
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Li ZZ, Guo C, Lv W, Huang P, Zhang Y. Machine Learning-Enabled Optical Architecture Design of Perovskite Solar Cells. J Phys Chem Lett 2024; 15:3835-3842. [PMID: 38557032 DOI: 10.1021/acs.jpclett.4c00320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Perovskite solar cells, emerging as a cutting-edge solar energy technology, have demonstrated a power conversion efficiency (PCE) of >26%, which is below the theoretical limit of 33%. This study, employing a combination of neural network models and high-throughput simulation calculations, taking the single-junction FAPbI3 cell as an illustrative example, indicates that a pyramid structure, in comparison of a planar one, can increase the highest Jsc to 27.4 mA/cm2 and the PCE to 28.4%. Both Jsc and PCE surpass the currently reported experimental results. The optimized periodicity and tilt angle of the pyramid structure align with the textured structure of crystalline silicon solar cells. These results underscore the substantial development potential of neural network inverse design based on high-throughput calculations in the field of optoelectronic devices and provide theoretical guidance for the design of monolithic perovskite-silicon tandem solar cells.
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Affiliation(s)
- Zong-Zheng Li
- Beijing Key Laboratory of Nanophotonics & Ultrafine Optoelectronic Systems, School of Physics, Beijing Institute of Technology, Beijing 100081, China
- Research Institute of Frontier Science, Southwest Jiaotong University, Chengdu 610031, China
| | - Chaorong Guo
- Research Institute of Frontier Science, Southwest Jiaotong University, Chengdu 610031, China
| | - Wenlei Lv
- Research Institute of Frontier Science, Southwest Jiaotong University, Chengdu 610031, China
| | - Peng Huang
- Research Institute of Frontier Science, Southwest Jiaotong University, Chengdu 610031, China
| | - Yongyou Zhang
- Beijing Key Laboratory of Nanophotonics & Ultrafine Optoelectronic Systems, School of Physics, Beijing Institute of Technology, Beijing 100081, China
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15
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Liu M, Wang J, Hu J, Liu P, Niu H, Yan X, Li J, Yan H, Yang B, Sun Y, Chen C, Kresse G, Zuo L, Chen XQ. Layer-by-layer phase transformation in Ti 3O 5 revealed by machine-learning molecular dynamics simulations. Nat Commun 2024; 15:3079. [PMID: 38594273 PMCID: PMC11004112 DOI: 10.1038/s41467-024-47422-1] [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: 10/11/2023] [Accepted: 03/28/2024] [Indexed: 04/11/2024] Open
Abstract
Reconstructive phase transitions involving breaking and reconstruction of primary chemical bonds are ubiquitous and important for many technological applications. In contrast to displacive phase transitions, the dynamics of reconstructive phase transitions are usually slow due to the large energy barrier. Nevertheless, the reconstructive phase transformation from β- to λ-Ti3O5 exhibits an ultrafast and reversible behavior. Despite extensive studies, the underlying microscopic mechanism remains unclear. Here, we discover a kinetically favorable in-plane nucleated layer-by-layer transformation mechanism through metadynamics and large-scale molecular dynamics simulations. This is enabled by developing an efficient machine learning potential with near first-principles accuracy through an on-the-fly active learning method and an advanced sampling technique. Our results reveal that the β-λ phase transformation initiates with the formation of two-dimensional nuclei in the ab-plane and then proceeds layer-by-layer through a multistep barrier-lowering kinetic process via intermediate metastable phases. Our work not only provides important insight into the ultrafast and reversible nature of the β-λ transition, but also presents useful strategies and methods for tackling other complex structural phase transitions.
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Affiliation(s)
- Mingfeng Liu
- Shenyang National Laboratory for Materials Science, Institute of Metal Research, Chinese Academy of Sciences, Shenyang, 110016, China
- School of Materials Science and Engineering, University of Science and Technology of China, Shenyang, 110016, China
| | - Jiantao Wang
- Shenyang National Laboratory for Materials Science, Institute of Metal Research, Chinese Academy of Sciences, Shenyang, 110016, China
- School of Materials Science and Engineering, University of Science and Technology of China, Shenyang, 110016, China
| | - Junwei Hu
- State Key Laboratory of Solidification Processing, International Center for Materials Discovery, School of Materials Science and Engineering, Northwestern Polytechnical University, Xi'an, 710072, China
| | - Peitao Liu
- Shenyang National Laboratory for Materials Science, Institute of Metal Research, Chinese Academy of Sciences, Shenyang, 110016, China.
| | - Haiyang Niu
- State Key Laboratory of Solidification Processing, International Center for Materials Discovery, School of Materials Science and Engineering, Northwestern Polytechnical University, Xi'an, 710072, China.
| | - Xuexi Yan
- Shenyang National Laboratory for Materials Science, Institute of Metal Research, Chinese Academy of Sciences, Shenyang, 110016, China
| | - Jiangxu Li
- Shenyang National Laboratory for Materials Science, Institute of Metal Research, Chinese Academy of Sciences, Shenyang, 110016, China
| | - Haile Yan
- Key Laboratory for Anisotropy and Texture of Materials (Ministry of Education), School of Materials Science and Engineering, Northeastern University, Shenyang, 110819, China
| | - Bo Yang
- Key Laboratory for Anisotropy and Texture of Materials (Ministry of Education), School of Materials Science and Engineering, Northeastern University, Shenyang, 110819, China
| | - Yan Sun
- Shenyang National Laboratory for Materials Science, Institute of Metal Research, Chinese Academy of Sciences, Shenyang, 110016, China
| | - Chunlin Chen
- Shenyang National Laboratory for Materials Science, Institute of Metal Research, Chinese Academy of Sciences, Shenyang, 110016, China
| | - Georg Kresse
- University of Vienna, Faculty of Physics and Center for Computational Materials Science, Kolingasse 14-16, A-1090, Vienna, Austria
| | - Liang Zuo
- Key Laboratory for Anisotropy and Texture of Materials (Ministry of Education), School of Materials Science and Engineering, Northeastern University, Shenyang, 110819, China
| | - Xing-Qiu Chen
- Shenyang National Laboratory for Materials Science, Institute of Metal Research, Chinese Academy of Sciences, Shenyang, 110016, China
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16
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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.
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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
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17
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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.
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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
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18
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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.
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19
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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.
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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.
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20
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Wang Y, Inizan TJ, Liu C, Piquemal JP, Ren P. Incorporating Neural Networks into the AMOEBA Polarizable Force Field. J Phys Chem B 2024; 128:2381-2388. [PMID: 38445577 PMCID: PMC10985787 DOI: 10.1021/acs.jpcb.3c08166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/07/2024]
Abstract
Neural network potentials (NNPs) offer significant promise to bridge the gap between the accuracy of quantum mechanics and the efficiency of molecular mechanics in molecular simulation. Most NNPs rely on the locality assumption that ensures the model's transferability and scalability and thus lack the treatment of long-range interactions, which are essential for molecular systems in the condensed phase. Here we present an integrated hybrid model, AMOEBA+NN, which combines the AMOEBA potential for the short- and long-range noncovalent atomic interactions and an NNP to capture the remaining local covalent contributions. The AMOEBA+NN model was trained on the conformational energy of the ANI-1x data set and tested on several external data sets ranging from small molecules to tetrapeptides. The hybrid model demonstrated substantial improvements over the baseline models in term of accuracy as the molecule size increased, suggesting its potential as a next-generation approach for chemically accurate molecular simulations.
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Affiliation(s)
- Yanxing Wang
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Théo Jaffrelot Inizan
- Sorbonne Université, Laboratoire de Chimie Théorique, UMR 7616 CNRS, Paris 75005, France
| | - Chengwen Liu
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Jean-Philip Piquemal
- Sorbonne Université, Laboratoire de Chimie Théorique, UMR 7616 CNRS, Paris 75005, France
| | - Pengyu Ren
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
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21
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Ruiz-Pernía JJ, Świderek K, Bertran J, Moliner V, Tuñón I. Electrostatics as a Guiding Principle in Understanding and Designing Enzymes. J Chem Theory Comput 2024; 20:1783-1795. [PMID: 38410913 PMCID: PMC10938506 DOI: 10.1021/acs.jctc.3c01395] [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: 12/21/2023] [Revised: 02/14/2024] [Accepted: 02/14/2024] [Indexed: 02/28/2024]
Abstract
Enzyme design faces challenges related to the implementation of the basic principles that govern the catalytic activity in natural enzymes. In this work, we revisit basic electrostatic concepts that have been shown to explain the origin of enzymatic efficiency like preorganization and reorganization. Using magnitudes such as the electrostatic potential and the electric field generated by the protein, we explain how these concepts work in different enzymes and how they can be used to rationalize the consequences of point mutations. We also discuss examples of protein design in which electrostatic effects have been implemented. For the near future, molecular simulations, coupled with the use of machine learning methods, can be used to implement electrostatics as a guiding principle for enzyme designs.
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Affiliation(s)
| | - Katarzyna Świderek
- Biocomp
group, Institute of Advanced Materials (INAM), Universitat Jaume I, 12071 Castellón Spain
| | - Joan Bertran
- Departament
de Química, Universitat Autònoma
de Barcelona, 08193 Bellaterra, Spain
| | - Vicent Moliner
- Biocomp
group, Institute of Advanced Materials (INAM), Universitat Jaume I, 12071 Castellón Spain
| | - Iñaki Tuñón
- Departament
de Química Física, Universitat
de València, 46100 Burjassot, Spain
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22
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Noordhoek K, Bartel CJ. Accelerating the prediction of inorganic surfaces with machine learning interatomic potentials. NANOSCALE 2024. [PMID: 38470833 DOI: 10.1039/d3nr06468a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/14/2024]
Abstract
The surface properties of solid-state materials often dictate their functionality, especially for applications where nanoscale effects become important. The relevant surface(s) and their properties are determined, in large part, by the material's synthesis or operating conditions. These conditions dictate thermodynamic driving forces and kinetic rates responsible for yielding the observed surface structure and morphology. Computational surface science methods have long been applied to connect thermochemical conditions to surface phase stability, particularly in the heterogeneous catalysis and thin film growth communities. This review provides a brief introduction to first-principles approaches to compute surface phase diagrams before introducing emerging data-driven approaches. The remainder of the review focuses on the application of machine learning, predominantly in the form of learned interatomic potentials, to study complex surfaces. As machine learning algorithms and large datasets on which to train them become more commonplace in materials science, computational methods are poised to become even more predictive and powerful for modeling the complexities of inorganic surfaces at the nanoscale.
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Affiliation(s)
- Kyle Noordhoek
- Department of Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, MN, 55455, USA.
| | - Christopher J Bartel
- Department of Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, MN, 55455, USA.
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23
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Li R, Zhou C, Singh A, Pei Y, Henkelman G, Li L. Local-environment-guided selection of atomic structures for the development of machine-learning potentials. J Chem Phys 2024; 160:074109. [PMID: 38380745 DOI: 10.1063/5.0187892] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Accepted: 01/26/2024] [Indexed: 02/22/2024] Open
Abstract
Machine learning potentials (MLPs) have attracted significant attention in computational chemistry and materials science due to their high accuracy and computational efficiency. The proper selection of atomic structures is crucial for developing reliable MLPs. Insufficient or redundant atomic structures can impede the training process and potentially result in a poor quality MLP. Here, we propose a local-environment-guided screening algorithm for efficient dataset selection in MLP development. The algorithm utilizes a local environment bank to store unique local environments of atoms. The dissimilarity between a particular local environment and those stored in the bank is evaluated using the Euclidean distance. A new structure is selected only if its local environment is significantly different from those already present in the bank. Consequently, the bank is then updated with all the new local environments found in the selected structure. To demonstrate the effectiveness of our algorithm, we applied it to select structures for a Ge system and a Pd13H2 particle system. The algorithm reduced the training data size by around 80% for both without compromising the performance of the MLP models. We verified that the results were independent of the selection and ordering of the initial structures. We also compared the performance of our method with the farthest point sampling algorithm, and the results show that our algorithm is superior in both robustness and computational efficiency. Furthermore, the generated local environment bank can be continuously updated and can potentially serve as a growing database of feature local environments, aiding in efficient dataset maintenance for constructing accurate MLPs.
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Affiliation(s)
- Renzhe Li
- Shenzhen Key Laboratory of Micro/Nano-Porous Functional Materials (SKLPM), Department of Materials Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, People's Republic of China
- College of Chemistry, Xiangtan University, Xiangtan 411105, Hunan Province, People's Republic of China
| | - Chuan Zhou
- Shenzhen Key Laboratory of Micro/Nano-Porous Functional Materials (SKLPM), Department of Materials Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, People's Republic of China
| | - Akksay Singh
- Shenzhen Key Laboratory of Micro/Nano-Porous Functional Materials (SKLPM), Department of Materials Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, People's Republic of China
- Department of Chemistry, The University of Texas at Austin, Austin, Texas 78712, USA
- Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas 78712, USA
| | - Yong Pei
- College of Chemistry, Xiangtan University, Xiangtan 411105, Hunan Province, People's Republic of China
| | - Graeme Henkelman
- Department of Chemistry, The University of Texas at Austin, Austin, Texas 78712, USA
- Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas 78712, USA
| | - Lei Li
- Shenzhen Key Laboratory of Micro/Nano-Porous Functional Materials (SKLPM), Department of Materials Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, People's Republic of China
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24
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Dral PO, Ge F, Hou YF, Zheng P, Chen Y, Barbatti M, Isayev O, Wang C, Xue BX, Pinheiro Jr M, Su Y, Dai Y, Chen Y, Zhang L, Zhang S, Ullah A, Zhang Q, Ou Y. MLatom 3: A Platform for Machine Learning-Enhanced Computational Chemistry Simulations and Workflows. J Chem Theory Comput 2024; 20:1193-1213. [PMID: 38270978 PMCID: PMC10867807 DOI: 10.1021/acs.jctc.3c01203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 12/29/2023] [Accepted: 01/03/2024] [Indexed: 01/26/2024]
Abstract
Machine learning (ML) is increasingly becoming a common tool in computational chemistry. At the same time, the rapid development of ML methods requires a flexible software framework for designing custom workflows. MLatom 3 is a program package designed to leverage the power of ML to enhance typical computational chemistry simulations and to create complex workflows. This open-source package provides plenty of choice to the users who can run simulations with the command-line options, input files, or with scripts using MLatom as a Python package, both on their computers and on the online XACS cloud computing service at XACScloud.com. Computational chemists can calculate energies and thermochemical properties, optimize geometries, run molecular and quantum dynamics, and simulate (ro)vibrational, one-photon UV/vis absorption, and two-photon absorption spectra with ML, quantum mechanical, and combined models. The users can choose from an extensive library of methods containing pretrained ML models and quantum mechanical approximations such as AIQM1 approaching coupled-cluster accuracy. The developers can build their own models using various ML algorithms. The great flexibility of MLatom is largely due to the extensive use of the interfaces to many state-of-the-art software packages and libraries.
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Affiliation(s)
- Pavlo O. Dral
- State
Key Laboratory of Physical Chemistry of Solid Surfaces, College of
Chemistry and Chemical Engineering, and Innovation Laboratory for
Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen, Fujian 361005, China
- Fujian
Provincial Key Laboratory of Theoretical and Computational Chemistry, Xiamen, Fujian 361005, China
| | - Fuchun Ge
- State
Key Laboratory of Physical Chemistry of Solid Surfaces, College of
Chemistry and Chemical Engineering, and Innovation Laboratory for
Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen, Fujian 361005, China
- Fujian
Provincial Key Laboratory of Theoretical and Computational Chemistry, Xiamen, Fujian 361005, China
| | - Yi-Fan Hou
- State
Key Laboratory of Physical Chemistry of Solid Surfaces, College of
Chemistry and Chemical Engineering, and Innovation Laboratory for
Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen, Fujian 361005, China
- Fujian
Provincial Key Laboratory of Theoretical and Computational Chemistry, Xiamen, Fujian 361005, China
| | - Peikun Zheng
- State
Key Laboratory of Physical Chemistry of Solid Surfaces, College of
Chemistry and Chemical Engineering, and Innovation Laboratory for
Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen, Fujian 361005, China
- Fujian
Provincial Key Laboratory of Theoretical and Computational Chemistry, Xiamen, Fujian 361005, China
| | - Yuxinxin Chen
- State
Key Laboratory of Physical Chemistry of Solid Surfaces, College of
Chemistry and Chemical Engineering, and Innovation Laboratory for
Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen, Fujian 361005, China
- Fujian
Provincial Key Laboratory of Theoretical and Computational Chemistry, Xiamen, Fujian 361005, China
| | - Mario Barbatti
- Aix
Marseille University, CNRS, ICR, Marseille 13013, France
- Institut
Universitaire de France, Paris 75231, France
| | - Olexandr Isayev
- Department
of Chemistry, Carnegie Mellon University, Pittsburgh, Pennsylvania15213, United States
| | - Cheng Wang
- State
Key Laboratory of Physical Chemistry of Solid Surfaces, College of
Chemistry and Chemical Engineering, and Innovation Laboratory for
Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen, Fujian 361005, China
- iChem, Xiamen University, Xiamen, Fujian 361005, China
| | - Bao-Xin Xue
- State
Key Laboratory of Physical Chemistry of Solid Surfaces, College of
Chemistry and Chemical Engineering, and Innovation Laboratory for
Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen, Fujian 361005, China
- Fujian
Provincial Key Laboratory of Theoretical and Computational Chemistry, Xiamen, Fujian 361005, China
| | - Max Pinheiro Jr
- Aix
Marseille University, CNRS, ICR, Marseille 13013, France
| | - Yuming Su
- State
Key Laboratory of Physical Chemistry of Solid Surfaces, College of
Chemistry and Chemical Engineering, and Innovation Laboratory for
Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen, Fujian 361005, China
- iChem, Xiamen University, Xiamen, Fujian 361005, China
| | - Yiheng Dai
- State
Key Laboratory of Physical Chemistry of Solid Surfaces, College of
Chemistry and Chemical Engineering, and Innovation Laboratory for
Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen, Fujian 361005, China
- iChem, Xiamen University, Xiamen, Fujian 361005, China
| | - Yangtao Chen
- State
Key Laboratory of Physical Chemistry of Solid Surfaces, College of
Chemistry and Chemical Engineering, and Innovation Laboratory for
Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen, Fujian 361005, China
- iChem, Xiamen University, Xiamen, Fujian 361005, China
| | - Lina Zhang
- State
Key Laboratory of Physical Chemistry of Solid Surfaces, College of
Chemistry and Chemical Engineering, and Innovation Laboratory for
Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen, Fujian 361005, China
- Fujian
Provincial Key Laboratory of Theoretical and Computational Chemistry, Xiamen, Fujian 361005, China
| | - Shuang Zhang
- State
Key Laboratory of Physical Chemistry of Solid Surfaces, College of
Chemistry and Chemical Engineering, and Innovation Laboratory for
Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen, Fujian 361005, China
- Fujian
Provincial Key Laboratory of Theoretical and Computational Chemistry, Xiamen, Fujian 361005, China
| | - Arif Ullah
- School
of Physics and Optoelectronic Engineering, Anhui University, Hefei230601, China
| | - Quanhao Zhang
- State
Key Laboratory of Physical Chemistry of Solid Surfaces, College of
Chemistry and Chemical Engineering, and Innovation Laboratory for
Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen, Fujian 361005, China
- Fujian
Provincial Key Laboratory of Theoretical and Computational Chemistry, Xiamen, Fujian 361005, China
| | - Yanchi Ou
- State
Key Laboratory of Physical Chemistry of Solid Surfaces, College of
Chemistry and Chemical Engineering, and Innovation Laboratory for
Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen, Fujian 361005, China
- Fujian
Provincial Key Laboratory of Theoretical and Computational Chemistry, Xiamen, Fujian 361005, China
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25
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Butler PV, Hafizi R, Day GM. Machine-Learned Potentials by Active Learning from Organic Crystal Structure Prediction Landscapes. J Phys Chem A 2024; 128:945-957. [PMID: 38277275 PMCID: PMC10860135 DOI: 10.1021/acs.jpca.3c07129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Revised: 01/04/2024] [Accepted: 01/11/2024] [Indexed: 01/28/2024]
Abstract
A primary challenge in organic molecular crystal structure prediction (CSP) is accurately ranking the energies of potential structures. While high-level solid-state density functional theory (DFT) methods allow for mostly reliable discrimination of the low-energy structures, their high computational cost is problematic because of the need to evaluate tens to hundreds of thousands of trial crystal structures to fully explore typical crystal energy landscapes. Consequently, lower-cost but less accurate empirical force fields are often used, sometimes as the first stage of a hierarchical scheme involving multiple stages of increasingly accurate energy calculations. Machine-learned interatomic potentials (MLIPs), trained to reproduce the results of ab initio methods with computational costs close to those of force fields, can improve the efficiency of the CSP by reducing or eliminating the need for costly DFT calculations. Here, we investigate active learning methods for training MLIPs with CSP datasets. The combination of active learning with the well-developed sampling methods from CSP yields potentials in a highly automated workflow that are relevant over a wide range of the crystal packing space. To demonstrate these potentials, we illustrate efficiently reranking large, diverse crystal structure landscapes to near-DFT accuracy from force field-based CSP, improving the reliability of the final energy ranking. Furthermore, we demonstrate how these potentials can be extended to more accurately model structures far from lattice energy minima through additional on-the-fly training within Monte Carlo simulations.
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Affiliation(s)
| | - Roohollah Hafizi
- School of Chemistry, University
of Southampton, Southampton SO17 1BJ, U.K.
| | - Graeme M. Day
- School of Chemistry, University
of Southampton, Southampton SO17 1BJ, U.K.
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26
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Sahre MJ, von Rudorff GF, Marquetand P, von Lilienfeld OA. Transferability of atomic energies from alchemical decomposition. J Chem Phys 2024; 160:054106. [PMID: 38341696 DOI: 10.1063/5.0187298] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Accepted: 01/09/2024] [Indexed: 02/13/2024] Open
Abstract
We study alchemical atomic energy partitioning as a method to estimate atomization energies from atomic contributions, which are defined in physically rigorous and general ways through the use of the uniform electron gas as a joint reference. We analyze quantitatively the relation between atomic energies and their local environment using a dataset of 1325 organic molecules. The atomic energies are transferable across various molecules, enabling the prediction of atomization energies with a mean absolute error of 23 kcal/mol, comparable to simple statistical estimates but potentially more robust given their grounding in the physics-based decomposition scheme. A comparative analysis with other decomposition methods highlights its sensitivity to electrostatic variations, underlining its potential as a representation of the environment as well as in studying processes like diffusion in solids characterized by significant electrostatic shifts.
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Affiliation(s)
- Michael J Sahre
- Vienna Doctoral School in Chemistry (DoSChem) and Institute of Theoretical Chemistry and Faculty of Physics, University of Vienna, 1090 Vienna, Austria
| | - Guido Falk von Rudorff
- Department of Chemistry, University Kassel, Heinrich-Plett-Str.40, 34132 Kassel, Germany
- Center for Interdisciplinary Nanostructure Science and Technology (CINSaT), Heinrich-Plett-Straße 40, 34132 Kassel, Germany
| | - Philipp Marquetand
- Faculty of Chemistry, Institute of Theoretical Chemistry, University of Vienna, Währinger Str. 17, 1090 Vienna, Austria
| | - O Anatole von Lilienfeld
- Vienna Doctoral School in Chemistry (DoSChem) and Institute of Theoretical Chemistry and Faculty of Physics, University of Vienna, 1090 Vienna, Austria
- Chemical Physics Theory Group, Department of Chemistry, University of Toronto, St. George Campus, Toronto, M5S 3H6 Ontario, Canada
- Department of Materials Science and Engineering, University of Toronto, St. George Campus, Toronto, M5S 3E4 Ontario, Canada
- Vector Institute for Artificial Intelligence, Toronto, M5S 1M1 Ontario, Canada
- ML Group, Technische Universität Berlin and Institute for the Foundations of Learning and Data, 10587 Berlin, Germany
- Berlin Institute for the Foundations of Learning and Data, 10587 Berlin, Germany
- Department of Physics, University of Toronto, St. George Campus, Toronto, M5S 1A7 Ontario, Canada
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27
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Xu W, Tao Y, Xu H, Wen J. Theoretical trends in the dynamics simulations of molecular machines across multiple scales. Phys Chem Chem Phys 2024; 26:4828-4839. [PMID: 38235540 DOI: 10.1039/d3cp05201j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
Abstract
Over the past few decades, molecular machines have been extensively studied, since they are composed of single molecules for functional materials capable of responding to external stimuli, enabling motion at scales ranging from the microscopic to the macroscopic level within molecular aggregates. This advancement holds the potential to efficiently transform external resources into mechanical movement, achieved through precise control of conformational changes in stimuli-responsive materials. However, the underlying mechanism that links microscopic and macroscopic motions remains unclear, demanding computational development associated with simulating the construction of molecular machines from single molecules. This bottleneck has impeded the design of more efficient functional materials. Advancements in theoretical simulations have successfully been developed in various computational models to unveil the operational mechanisms of stimulus-responsive molecular machines, which could help us reduce the costs in experimental trial-and-error procedures. It opens doors to the computer-aided design of innovative functional materials. In this perspective, we have reviewed theoretical approaches employed in simulating dynamic processes involving conformational changes in molecular machines, spanning different scales and environmental conditions. In addition, we have highlighted current challenges and anticipated future trends in the collective control of aggregates within molecular machines. Our goal is to provide a comprehensive overview of recent theoretical advancements in the field of molecular machines, offering valuable insights for the design of novel smart materials.
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Affiliation(s)
- Weijia Xu
- State Key Laboratory for Modification of Chemical Fibers and Polymer Materials, College of Materials Science and Engineering, Donghua University, Shanghai 201620, China.
| | - Yuanda Tao
- State Key Laboratory for Modification of Chemical Fibers and Polymer Materials, College of Materials Science and Engineering, Donghua University, Shanghai 201620, China.
| | - Haoyang Xu
- State Key Laboratory for Modification of Chemical Fibers and Polymer Materials, College of Materials Science and Engineering, Donghua University, Shanghai 201620, China.
| | - Jin Wen
- State Key Laboratory for Modification of Chemical Fibers and Polymer Materials, College of Materials Science and Engineering, Donghua University, Shanghai 201620, China.
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28
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Kapil V, Kovács DP, Csányi G, Michaelides A. First-principles spectroscopy of aqueous interfaces using machine-learned electronic and quantum nuclear effects. Faraday Discuss 2024; 249:50-68. [PMID: 37799072 PMCID: PMC10845015 DOI: 10.1039/d3fd00113j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Accepted: 07/18/2023] [Indexed: 10/07/2023]
Abstract
Vibrational spectroscopy is a powerful approach to visualising interfacial phenomena. However, extracting structural and dynamical information from vibrational spectra is a challenge that requires first-principles simulations, including non-Condon and quantum nuclear effects. We address this challenge by developing a machine-learning enhanced first-principles framework to speed up predictive modelling of infrared, Raman, and sum-frequency generation spectra. Our approach uses machine learning potentials that encode quantum nuclear effects to generate quantum trajectories using simple molecular dynamics efficiently. In addition, we reformulate bulk and interfacial selection rules to express them unambiguously in terms of the derivatives of polarisation and polarisabilities of the whole system and predict these derivatives efficiently using fully-differentiable machine learning models of dielectric response tensors. We demonstrate our framework's performance by predicting the IR, Raman, and sum-frequency generation spectra of liquid water, ice and the water-air interface by achieving near quantitative agreement with experiments at nearly the same computational efficiency as pure classical methods. Finally, to aid the experimental discovery of new phases of nanoconfined water, we predict the temperature-dependent vibrational spectra of monolayer water across the solid-hexatic-liquid phases transition.
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Affiliation(s)
- Venkat Kapil
- Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK.
| | | | - Gábor Csányi
- Engineering Laboratory, University of Cambridge, Cambridge, CB2 1PZ, UK
| | - Angelos Michaelides
- Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK.
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29
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Tropsha A, Isayev O, Varnek A, Schneider G, Cherkasov A. Integrating QSAR modelling and deep learning in drug discovery: the emergence of deep QSAR. Nat Rev Drug Discov 2024; 23:141-155. [PMID: 38066301 DOI: 10.1038/s41573-023-00832-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/21/2023] [Indexed: 02/08/2024]
Abstract
Quantitative structure-activity relationship (QSAR) modelling, an approach that was introduced 60 years ago, is widely used in computer-aided drug design. In recent years, progress in artificial intelligence techniques, such as deep learning, the rapid growth of databases of molecules for virtual screening and dramatic improvements in computational power have supported the emergence of a new field of QSAR applications that we term 'deep QSAR'. Marking a decade from the pioneering applications of deep QSAR to tasks involved in small-molecule drug discovery, we herein describe key advances in the field, including deep generative and reinforcement learning approaches in molecular design, deep learning models for synthetic planning and the application of deep QSAR models in structure-based virtual screening. We also reflect on the emergence of quantum computing, which promises to further accelerate deep QSAR applications and the need for open-source and democratized resources to support computer-aided drug design.
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Affiliation(s)
| | | | | | | | - Artem Cherkasov
- University of British Columbia, Vancouver, BC, Canada.
- Photonic Inc., Coquitlam, BC, Canada.
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30
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Liebetrau M, Dorenkamp Y, Bünermann O, Behler J. Hydrogen atom scattering at the Al 2O 3(0001) surface: a combined experimental and theoretical study. Phys Chem Chem Phys 2024; 26:1696-1708. [PMID: 38126723 DOI: 10.1039/d3cp04729f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2023]
Abstract
Investigating atom-surface interactions is the key to an in-depth understanding of chemical processes at interfaces, which are of central importance in many fields - from heterogeneous catalysis to corrosion. In this work, we present a joint experimental and theoretical effort to gain insights into the atomistic details of hydrogen atom scattering at the α-Al2O3(0001) surface. Surprisingly, this system has been hardly studied to date, although hydrogen atoms as well as α-Al2O3 are omnipresent in catalysis as reactive species and support oxide, respectively. We address this system by performing hydrogen atom beam scattering experiments and molecular dynamics (MD) simulations based on a high-dimensional machine learning potential trained to density functional theory data. Using this combination of methods we are able to probe the properties of the multidimensional potential energy surface governing the scattering process. Specifically, we compare the angular distribution and the kinetic energy loss of the scattered atoms obtained in experiment with a large number of MD trajectories, which, moreover, allow to identify the underlying impact sites at the surface.
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Affiliation(s)
- Martin Liebetrau
- Lehrstuhl für Theoretische Chemie II, Ruhr-Universität Bochum, D-44780 Bochum, Germany.
- Research Center Chemical Sciences and Sustainability, Research Alliance Ruhr, D-44780 Bochum, Germany
| | - Yvonne Dorenkamp
- Georg-August-Universität Göttingen, Institut für Physikalische Chemie, Tammannstraße 6, D-37077 Göttingen, Germany.
| | - Oliver Bünermann
- Georg-August-Universität Göttingen, Institut für Physikalische Chemie, Tammannstraße 6, D-37077 Göttingen, Germany.
- Department of Dynamics at Surfaces, Max-Planck-Institute for Multidisciplinary Sciences, Am Fassberg 11, D-37007 Göttingen, Germany
- International Center of Advanced Studies of Energy Conversion, Georg-August-Universität Göttingen, Tammannstraße 6, D-37077 Göttingen, Germany
| | - Jörg Behler
- Lehrstuhl für Theoretische Chemie II, Ruhr-Universität Bochum, D-44780 Bochum, Germany.
- Research Center Chemical Sciences and Sustainability, Research Alliance Ruhr, D-44780 Bochum, Germany
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31
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Manzhos S, Ihara M. Degeneration of kernel regression with Matern kernels into low-order polynomial regression in high dimension. J Chem Phys 2024; 160:021101. [PMID: 38189605 DOI: 10.1063/5.0187867] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Accepted: 12/17/2023] [Indexed: 01/09/2024] Open
Abstract
Kernel methods such as kernel ridge regression and Gaussian process regression with Matern-type kernels have been increasingly used, in particular, to fit potential energy surfaces (PES) and density functionals, and for materials informatics. When the dimensionality of the feature space is high, these methods are used with necessarily sparse data. In this regime, the optimal length parameter of a Matern-type kernel may become so large that the method effectively degenerates into a low-order polynomial regression and, therefore, loses any advantage over such regression. This is demonstrated theoretically as well as numerically in the examples of six- and fifteen-dimensional molecular PES using squared exponential and simple exponential kernels. The results shed additional light on the success of polynomial approximations such as PIP for medium-size molecules and on the importance of orders-of-coupling-based models for preserving the advantages of kernel methods with Matern-type kernels of on the use of physically motivated (reproducing) kernels.
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Affiliation(s)
- Sergei Manzhos
- School of Materials and Chemical Technology, Tokyo Institute of Technology, Ookayama 2-12-1, Meguro-ku, Tokyo 152-8552, Japan
| | - Manabu Ihara
- School of Materials and Chemical Technology, Tokyo Institute of Technology, Ookayama 2-12-1, Meguro-ku, Tokyo 152-8552, Japan
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32
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Yu Y, Yang D, Zhou Y, Xie D. A New Full-Dimensional Ab Initio Intermolecular Potential Energy Surface and Rovibrational Energies of the H 2O-H 2 Complex. J Phys Chem A 2024; 128:170-181. [PMID: 38109882 DOI: 10.1021/acs.jpca.3c06805] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2023]
Abstract
H2O-H2 is a prototypical five-atom van der Waals system, and the interaction between H2O and H2 plays an important role in many physical and chemical environments. However, previous full-dimensional intermolecular potential energy surfaces (IPESs) cannot accurately describe the H2O-H2 interaction in the repulsive or van der Waals minimum region. In this work, we constructed a full-dimensional IPES for the title system with a small root-mean-square error of 0.252 cm-1 by using the permutation invariant polynomial neural network method. The ab initio calculations were performed by employing the explicitly corrected coupled cluster [CCSD(T)-F12a] method with the augmented correlation-consistent polarized valence quintuple-ζ basis set. Based on the newly developed IPES, the bound states of the H2O-H2 complex were calculated within the rigid-rotor approximation. The transition frequencies and band origins agreed well with the experimental values [Weida, M. J.; Nesbitt, D. J. J. Chem. Phys. 1999, 110, 156-167] with errors less than 0.1 cm-1 for most transitions. Those results demonstrate the high accuracy of our new IPES, which would build a solid foundation for the collisional dynamics of H2O-H2 at low temperatures.
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Affiliation(s)
- Yipeng Yu
- Institute of Theoretical and Computational Chemistry, Key Laboratory of Mesoscopic Chemistry, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, China
| | - Dongzheng Yang
- Department of Chemistry and Chemical Biology, University of New Mexico, Albuquerque, New Mexico 87131, United States
| | - Yanzi Zhou
- Institute of Theoretical and Computational Chemistry, Key Laboratory of Mesoscopic Chemistry, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, China
| | - Daiqian Xie
- Institute of Theoretical and Computational Chemistry, Key Laboratory of Mesoscopic Chemistry, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, China
- Hefei National Laboratory, Hefei 230088, China
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33
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Gelžinytė E, Öeren M, Segall MD, Csányi G. Transferable Machine Learning Interatomic Potential for Bond Dissociation Energy Prediction of Drug-like Molecules. J Chem Theory Comput 2024; 20:164-177. [PMID: 38108269 PMCID: PMC10782450 DOI: 10.1021/acs.jctc.3c00710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Revised: 11/30/2023] [Accepted: 11/30/2023] [Indexed: 12/19/2023]
Abstract
We present a transferable MACE interatomic potential that is applicable to open- and closed-shell drug-like molecules containing hydrogen, carbon, and oxygen atoms. Including an accurate description of radical species extends the scope of possible applications to bond dissociation energy (BDE) prediction, for example, in the context of cytochrome P450 (CYP) metabolism. The transferability of the MACE potential was validated on the COMP6 data set, containing only closed-shell molecules, where it reaches better accuracy than the readily available general ANI-2x potential. MACE achieves similar accuracy on two CYP metabolism-specific data sets, which include open- and closed-shell structures. This model enables us to calculate the aliphatic C-H BDE, which allows us to compare reaction energies of hydrogen abstraction, which is the rate-limiting step of the aliphatic hydroxylation reaction catalyzed by CYPs. On the "CYP 3A4" data set, MACE achieves a BDE RMSE of 1.37 kcal/mol and better prediction of BDE ranks than alternatives: the semiempirical AM1 and GFN2-xTB methods and the ALFABET model that directly predicts bond dissociation enthalpies. Finally, we highlight the smoothness of the MACE potential over paths of sp3C-H bond elongation and show that a minimal extension is enough for the MACE model to start finding reasonable minimum energy paths of methoxy radical-mediated hydrogen abstraction. Altogether, this work lays the ground for further extensions of scope in terms of chemical elements, (CYP-mediated) reaction classes and modeling the full reaction paths, not only BDEs.
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Affiliation(s)
- Elena Gelžinytė
- Engineering
Laboratory, University of Cambridge, Trumpington Street, Cambridge CB2 1PZ, U.K.
| | - Mario Öeren
- Optibrium
Limited, Cambridge Innovation Park, Denny End Road, Cambridge CB25 9GL, U.K.
| | - Matthew D. Segall
- Optibrium
Limited, Cambridge Innovation Park, Denny End Road, Cambridge CB25 9GL, U.K.
| | - Gábor Csányi
- Engineering
Laboratory, University of Cambridge, Trumpington Street, Cambridge CB2 1PZ, U.K.
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34
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Dufils T, Schran C, Chen J, Geim AK, Fumagalli L, Michaelides A. Origin of dielectric polarization suppression in confined water from first principles. Chem Sci 2024; 15:516-527. [PMID: 38179530 PMCID: PMC10763014 DOI: 10.1039/d3sc04740g] [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: 09/07/2023] [Accepted: 11/23/2023] [Indexed: 01/06/2024] Open
Abstract
It has long been known that the dielectric constant of confined water should be different from that in bulk. Recent experiments have shown that it is vanishingly small, however the origin of the phenomenon remains unclear. Here we used ab initio molecular dynamics simulations (AIMD) and AIMD-trained machine-learning potentials to understand water's structure and electronic properties underpinning this effect. For the graphene and hexagonal boron-nitride substrates considered, we find that it originates in the spontaneous anti-parallel alignment of the water dipoles in the first two water layers near the solid interface. The interfacial layers exhibit net ferroelectric ordering, resulting in an overall anti-ferroelectric arrangement of confined water. Together with constrained hydrogen-bonding orientations, this leads to much reduced out-of-plane polarization. Furthermore, we directly contrast AIMD and simple classical force-field simulations, revealing important differences. This work offers insight into a property of water that is critical in modulating surface forces, the electric-double-layer formation and molecular solvation, and shows a way to compute it.
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Affiliation(s)
- T Dufils
- Department of Physics and Astronomy, University of Manchester Manchester M13 9PL UK
- National Graphene Institute, University of Manchester Manchester M13 9PL UK
| | - C Schran
- Cavendish Laboratory, Department of Physics, University of Cambridge Cambridge CB3 0HE UK
- Lennard-Jones Centre, University of Cambridge Trinity Ln Cambridge CB2 1TN UK
| | - J Chen
- School of Physics, Peking University Beijing 100871 China
| | - A K Geim
- Department of Physics and Astronomy, University of Manchester Manchester M13 9PL UK
- National Graphene Institute, University of Manchester Manchester M13 9PL UK
| | - L Fumagalli
- Department of Physics and Astronomy, University of Manchester Manchester M13 9PL UK
- National Graphene Institute, University of Manchester Manchester M13 9PL UK
| | - A Michaelides
- Lennard-Jones Centre, University of Cambridge Trinity Ln Cambridge CB2 1TN UK
- Yusuf Hamied Department of Chemistry, University of Cambridge Lensfield Road Cambridge CB2 1EW UK
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35
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Stark W, Westermayr J, Douglas-Gallardo OA, Gardner J, Habershon S, Maurer RJ. Machine Learning Interatomic Potentials for Reactive Hydrogen Dynamics at Metal Surfaces Based on Iterative Refinement of Reaction Probabilities. THE JOURNAL OF PHYSICAL CHEMISTRY. C, NANOMATERIALS AND INTERFACES 2023; 127:24168-24182. [PMID: 38148847 PMCID: PMC10749455 DOI: 10.1021/acs.jpcc.3c06648] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Revised: 11/12/2023] [Accepted: 11/15/2023] [Indexed: 12/28/2023]
Abstract
The reactive chemistry of molecular hydrogen at surfaces, notably dissociative sticking and hydrogen evolution, plays a crucial role in energy storage and fuel cells. Theoretical studies can help to decipher underlying mechanisms and reaction design, but studying dynamics at surfaces is computationally challenging due to the complex electronic structure at interfaces and the high sensitivity of dynamics to reaction barriers. In addition, ab initio molecular dynamics, based on density functional theory, is too computationally demanding to accurately predict reactive sticking or desorption probabilities, as it requires averaging over tens of thousands of initial conditions. High-dimensional machine learning-based interatomic potentials are starting to be more commonly used in gas-surface dynamics, yet robust approaches to generate reliable training data and assess how model uncertainty affects the prediction of dynamic observables are not well established. Here, we employ ensemble learning to adaptively generate training data while assessing model performance with full uncertainty quantification (UQ) for reaction probabilities of hydrogen scattering on different copper facets. We use this approach to investigate the performance of two message-passing neural networks, SchNet and PaiNN. Ensemble-based UQ and iterative refinement allow us to expose the shortcomings of the invariant pairwise-distance-based feature representation in the SchNet model for gas-surface dynamics.
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Affiliation(s)
- Wojciech
G. Stark
- Department
of Chemistry, University of Warwick, Gibbet Hill Road, Coventry CV4 7AL, U.K.
| | - Julia Westermayr
- Department
of Chemistry, University of Warwick, Gibbet Hill Road, Coventry CV4 7AL, U.K.
| | | | - James Gardner
- Department
of Chemistry, University of Warwick, Gibbet Hill Road, Coventry CV4 7AL, U.K.
| | - Scott Habershon
- Department
of Chemistry, University of Warwick, Gibbet Hill Road, Coventry CV4 7AL, U.K.
| | - Reinhard J. Maurer
- Department
of Chemistry, University of Warwick, Gibbet Hill Road, Coventry CV4 7AL, U.K.
- Department
of Physics, University of Warwick, Gibbet Hill Road, Coventry CV4 7AL, U.K.
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36
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Fu B, Zhang DH. Accurate fundamental invariant-neural network representation of ab initio potential energy surfaces. Natl Sci Rev 2023; 10:nwad321. [PMID: 38274241 PMCID: PMC10808953 DOI: 10.1093/nsr/nwad321] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2023] [Revised: 11/01/2023] [Accepted: 11/02/2023] [Indexed: 01/27/2024] Open
Abstract
Highly accurate potential energy surfaces are critically important for chemical reaction dynamics. The large number of degrees of freedom and the intricate symmetry adaption pose a big challenge to accurately representing potential energy surfaces (PESs) for polyatomic reactions. Recently, our group has made substantial progress in this direction by developing the fundamental invariant-neural network (FI-NN) approach. Here, we review these advances, demonstrating that the FI-NN approach can represent highly accurate, global, full-dimensional PESs for reactive systems with even more than 10 atoms. These multi-channel reactions typically involve many intermediates, transition states, and products. The complexity and ruggedness of this potential energy landscape present even greater challenges for full-dimensional PES representation. These PESs exhibit a high level of complexity, molecular size, and accuracy of fit. Dynamics simulations based on these PESs have unveiled intriguing and novel reaction mechanisms, providing deep insights into the intricate dynamics involved in combustion, atmospheric, and organic chemistry.
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Affiliation(s)
- Bina Fu
- State Key Laboratory of Molecular Reaction Dynamics and Center for Theoretical and Computational Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China
- Hefei National Laboratory, Hefei 230088, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Dong H Zhang
- State Key Laboratory of Molecular Reaction Dynamics and Center for Theoretical and Computational Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China
- Hefei National Laboratory, Hefei 230088, China
- University of Chinese Academy of Sciences, Beijing 100049, China
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37
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Ricard TC, Zhu X, Iyengar SS. Capturing Weak Interactions in Surface Adsorbate Systems at Coupled Cluster Accuracy: A Graph-Theoretic Molecular Fragmentation Approach Improved through Machine Learning. J Chem Theory Comput 2023. [PMID: 38019639 DOI: 10.1021/acs.jctc.3c00955] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2023]
Abstract
The accurate and efficient study of the interactions of organic matter with the surface of water is critical to a wide range of applications. For example, environmental studies have found that acidic polyfluorinated alkyl substances, especially perfluorooctanoic acid (PFOA), have spread throughout the environment and bioaccumulate into human populations residing near contaminated watersheds, leading to many systemic maladies. Thus, the study of the interactions of PFOA with water surfaces became important for the mitigation of their activity as pollutants and threats to public health. However, theoretical study of the interactions of such organic adsorbates on the surface of water, and their bulk concerted properties, often necessitates the use of ab initio methods to properly incorporate the long-range electronic properties that govern these extended systems. Notable theoretical treatments of "on-water" reactions thus far have employed hybrid DFT and semilocal DFT, but the interactions involved are weak interactions that may be best described using post-Hartree-Fock theory. Here, we aim to demonstrate the utility of a graph-theoretic approach to molecular fragmentation that accurately captures the critical "weak" interactions while maintaining an efficient ab initio treatment of the long-range periodic interactions that underpin the physics of extended systems. We apply this graph-theoretical treatment to study PFOA on the surface of water as a model system for the study of weak interactions seen in the wide range of surface interactions and reactions. The approach divides a system into a set of vertices, that are then connected through edges, faces, and higher order graph theoretic objects known as simplexes, to represent a collection of locally interacting subsystems. These subsystems are then used to construct ab initio molecular dynamics simulations and for computing multidimensional potential energy surfaces. To further improve the computational efficiency of our graph theoretic fragmentation method, we use a recently developed transfer learning protocol to construct the full system potential energy from a family of neural networks each designed to accurately model the behavior of individual simplexes. We use a unique multidimensional clustering algorithm, based on the k-means clustering methodology, to define our training space for each separate simplex. These models are used to extrapolate the energies for molecular dynamics trajectories at PFOA water interfaces, at less than one-tenth the cost as compared to a regular molecular fragmentation-based dynamics calculation with excellent agreement with couple cluster level of full system potential energies.
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Affiliation(s)
- Timothy C Ricard
- Department of Chemistry and Department of Physics, Indiana University, 800 E. Kirkwood Avenue, Bloomington, Indiana 47405, United States
| | - Xiao Zhu
- Department of Chemistry and Department of Physics, Indiana University, 800 E. Kirkwood Avenue, Bloomington, Indiana 47405, United States
| | - Srinivasan S Iyengar
- Department of Chemistry and Department of Physics, Indiana University, 800 E. Kirkwood Avenue, Bloomington, Indiana 47405, United States
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38
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Xia J, Zhang Y, Jiang B. Accuracy Assessment of Atomistic Neural Network Potentials: The Impact of Cutoff Radius and Message Passing. J Phys Chem A 2023; 127:9874-9883. [PMID: 37943102 DOI: 10.1021/acs.jpca.3c06024] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2023]
Abstract
Atomistic neural network potentials have achieved great success in accelerating atomistic simulations in complicated systems in recent years. They are typically based on the atomic decomposition of total properties, truncating the interatomic correlations to a local environment within a given cutoff radius. A more recently developed message passing (MP) neural network framework can, in principle, incorporate nonlocal effects through iteratively correlating some atoms outside the cutoff sphere with atoms inside, a process referred to as MP. However, how the model accuracy depends on the cutoff radius and the MP process has rarely been discussed. In this work, we investigate this dependence using a recursively embedded atom neural network method that possesses both local and MP features, in two representative systems: liquid H2O and solid Al2O3. We focus on how these settings influence predictions for structural and vibrational properties, namely, radial distribution functions (RDFs) and vibrational density of states (VDOSs). We find that while MP lowers test errors of energy and forces in general, it may not improve the prediction for RDFs and/or VDOSs if direct interatomic correlations in the local environment are insufficiently described. A cutoff radius exceeding the first neighbor shell is necessary, beyond which involving MP quickly enhances the model accuracy until convergence. This is a potentially more efficient way to increase the model accuracy than directly increasing the cutoff radius, especially with more memory savings in the GPU implementation. Our findings also suggest that using the mean test error as the measure of the model accuracy alone is inadequate.
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Affiliation(s)
- Junfan Xia
- Key Laboratory of Precision and Intelligent Chemistry, Department of Chemical Physics, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Yaolong Zhang
- École Polytechnique FFlytech de Lausanne, 1015 Lausanne, Switzerland
| | - Bin Jiang
- Key Laboratory of Precision and Intelligent Chemistry, Department of Chemical Physics, University of Science and Technology of China, Hefei, Anhui 230026, China
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39
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Abedi M, Behler J, Goldsmith CF. High-Dimensional Neural Network Potentials for Accurate Prediction of Equation of State: A Case Study of Methane. J Chem Theory Comput 2023; 19:7825-7832. [PMID: 37902963 DOI: 10.1021/acs.jctc.3c00469] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/01/2023]
Abstract
Machine learning-based interatomic potentials, such as those provided by neural networks, are increasingly important in molecular dynamics simulations. In the present work, we consider the applicability and robustness of machine learning molecular dynamics to predict the equation of state properties of methane by using high-dimensional neural network potentials (HDNNPs). We investigate two different strategies for generating training data: one strategy based upon bulk representations using periodic cells and another strategy based upon clusters of molecules. We assess the accuracy of the trained potentials by predicting the equilibrium mass density for a wide range of thermodynamic conditions to characterize the liquid phase, supercritical fluid, and gas phase, as well as the liquid-vapor coexistence curve. Our results show an excellent agreement with reference phase diagrams, with an average error below ∼2% for all studied phases. Moreover, we confirm the applicability of models trained on cluster data sets for producing accurate and reliable results.
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Affiliation(s)
- Mostafa Abedi
- School of Engineering, Brown University, Providence, Rhode Island 02906, United States
| | - Jörg Behler
- Lehrstuhl für Theoretische Chemie II, Ruhr-Universität Bochum, 44780 Bochum, Germany
- Research Center Chemical Sciences and Sustainability, Research Alliance Ruhr, 44780 Bochum, Germany
| | - C Franklin Goldsmith
- School of Engineering, Brown University, Providence, Rhode Island 02906, United States
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40
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Illarionov A, Sakipov S, Pereyaslavets L, Kurnikov IV, Kamath G, Butin O, Voronina E, Ivahnenko I, Leontyev I, Nawrocki G, Darkhovskiy M, Olevanov M, Cherniavskyi YK, Lock C, Greenslade S, Sankaranarayanan SKRS, Kurnikova MG, Potoff J, Kornberg RD, Levitt M, Fain B. Combining Force Fields and Neural Networks for an Accurate Representation of Chemically Diverse Molecular Interactions. J Am Chem Soc 2023; 145:23620-23629. [PMID: 37856313 PMCID: PMC10623557 DOI: 10.1021/jacs.3c07628] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Indexed: 10/21/2023]
Abstract
A key goal of molecular modeling is the accurate reproduction of the true quantum mechanical potential energy of arbitrary molecular ensembles with a tractable classical approximation. The challenges are that analytical expressions found in general purpose force fields struggle to faithfully represent the intermolecular quantum potential energy surface at close distances and in strong interaction regimes; that the more accurate neural network approximations do not capture crucial physics concepts, e.g., nonadditive inductive contributions and application of electric fields; and that the ultra-accurate narrowly targeted models have difficulty generalizing to the entire chemical space. We therefore designed a hybrid wide-coverage intermolecular interaction model consisting of an analytically polarizable force field combined with a short-range neural network correction for the total intermolecular interaction energy. Here, we describe the methodology and apply the model to accurately determine the properties of water, the free energy of solvation of neutral and charged molecules, and the binding free energy of ligands to proteins. The correction is subtyped for distinct chemical species to match the underlying force field, to segment and reduce the amount of quantum training data, and to increase accuracy and computational speed. For the systems considered, the hybrid ab initio parametrized Hamiltonian reproduces the two-body dimer quantum mechanics (QM) energies to within 0.03 kcal/mol and the nonadditive many-molecule contributions to within 2%. Simulations of molecular systems using this interaction model run at speeds of several nanoseconds per day.
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Affiliation(s)
- Alexey Illarionov
- InterX
Inc. (a Subsidiary of NeoTX Therapeutics Ltd.), 805 Allston Way, Berkeley, California 94710, United States
| | - Serzhan Sakipov
- InterX
Inc. (a Subsidiary of NeoTX Therapeutics Ltd.), 805 Allston Way, Berkeley, California 94710, United States
| | - Leonid Pereyaslavets
- InterX
Inc. (a Subsidiary of NeoTX Therapeutics Ltd.), 805 Allston Way, Berkeley, California 94710, United States
| | - Igor V. Kurnikov
- InterX
Inc. (a Subsidiary of NeoTX Therapeutics Ltd.), 805 Allston Way, Berkeley, California 94710, United States
| | - Ganesh Kamath
- InterX
Inc. (a Subsidiary of NeoTX Therapeutics Ltd.), 805 Allston Way, Berkeley, California 94710, United States
| | - Oleg Butin
- InterX
Inc. (a Subsidiary of NeoTX Therapeutics Ltd.), 805 Allston Way, Berkeley, California 94710, United States
| | - Ekaterina Voronina
- InterX
Inc. (a Subsidiary of NeoTX Therapeutics Ltd.), 805 Allston Way, Berkeley, California 94710, United States
- Lomonosov
MSU, Skobeltsyn Institute of Nuclear Physics, Moscow, 119991, Russia
| | - Ilya Ivahnenko
- InterX
Inc. (a Subsidiary of NeoTX Therapeutics Ltd.), 805 Allston Way, Berkeley, California 94710, United States
| | - Igor Leontyev
- InterX
Inc. (a Subsidiary of NeoTX Therapeutics Ltd.), 805 Allston Way, Berkeley, California 94710, United States
| | - Grzegorz Nawrocki
- InterX
Inc. (a Subsidiary of NeoTX Therapeutics Ltd.), 805 Allston Way, Berkeley, California 94710, United States
| | - Mikhail Darkhovskiy
- InterX
Inc. (a Subsidiary of NeoTX Therapeutics Ltd.), 805 Allston Way, Berkeley, California 94710, United States
| | - Michael Olevanov
- InterX
Inc. (a Subsidiary of NeoTX Therapeutics Ltd.), 805 Allston Way, Berkeley, California 94710, United States
- Lomonosov
MSU, Dept. of Physics, Moscow, 119991, Russia
| | - Yevhen K. Cherniavskyi
- InterX
Inc. (a Subsidiary of NeoTX Therapeutics Ltd.), 805 Allston Way, Berkeley, California 94710, United States
| | - Christopher Lock
- InterX
Inc. (a Subsidiary of NeoTX Therapeutics Ltd.), 805 Allston Way, Berkeley, California 94710, United States
- Department
of Neurology and Neurological Sciences, Stanford University School of Medicine, Palo Alto, California 94304, United States
| | - Sean Greenslade
- InterX
Inc. (a Subsidiary of NeoTX Therapeutics Ltd.), 805 Allston Way, Berkeley, California 94710, United States
| | - Subramanian KRS Sankaranarayanan
- Center
for Nanoscale Materials, Argonne National
Lab, Argonne, Illinois 604391, United States
- Department
of Mechanical and Industrial Engineering, University of Illinois, Chicago, Illinois 60607, United States
| | - Maria G. Kurnikova
- Department
of Chemistry, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States
| | - Jeffrey Potoff
- Department
of Chemical Engineering and Materials Science, Wayne State University, Detroit, Michigan 48202, United States
| | - Roger D. Kornberg
- Department
of Structural Biology, Stanford University
School of Medicine, Stanford, California 94304, United States
| | - Michael Levitt
- Department
of Structural Biology, Stanford University
School of Medicine, Stanford, California 94304, United States
| | - Boris Fain
- InterX
Inc. (a Subsidiary of NeoTX Therapeutics Ltd.), 805 Allston Way, Berkeley, California 94710, United States
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41
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Raju RK, Sivakumar S, Wang X, Ulissi ZW. Cluster-MLP: An Active Learning Genetic Algorithm Framework for Accelerated Discovery of Global Minimum Configurations of Pure and Alloyed Nanoclusters. J Chem Inf Model 2023; 63:6192-6197. [PMID: 37824704 PMCID: PMC10598790 DOI: 10.1021/acs.jcim.3c01431] [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: 09/06/2023] [Indexed: 10/14/2023]
Abstract
Structural characterization of nanoclusters is one of the major challenges in nanocluster modeling owing to the multitude of possible configurations of arrangement of cluster atoms. The genetic algorithm (GA), a class of evolutionary algorithms based on the principles of natural evolution, is a commonly employed search method for locating the global minimum configuration of nanoclusters. Although a GA search at the DFT level is required for the accurate description of a potential energy surface to arrive at the correct global minimum configuration of nanoclusters, computationally expensive DFT evaluation of the significantly larger number of cluster geometries limits its practicability. Recently, machine learning potentials (MLP) that are learned from DFT calculations gained significant attention as computationally cheap alternative options that provide DFT level accuracy. As the accuracy of the MLP predictions is dependent on the quality and quantity of the training DFT data, active learning (AL) strategies have gained significant momentum to bypass the need of large and representative training data. In this application note, we present Cluster-MLP, an on-the-fly active learning genetic algorithm framework that employs the Flare++ machine learning potential (MLP) for accelerating the GA search for global minima of pure and alloyed nanoclusters. We have used a modified version the Birmingham parallel genetic algorithm (BPGA) for the nanocluster GA search which is then incorporated into distributed evolutionary algorithms in Python (DEAP), an evolutionary computational framework for fast prototyping or technical experiments. We have shown that the incorporation of the AL framework in the BPGA significantly reduced the computationally expensive DFT calculations. Moreover, we have shown that both the AL-GA and DFT-GA predict the same global minima for all the clusters we tested.
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Affiliation(s)
- Rajesh K. Raju
- Chemical
Engineering Department, Carnegie Mellon
University, Pittsburgh, Pennsylvania 15217, United States
- School
of Chemistry, University of Birmingham, Birmingham B15 2TT, United Kingdom
| | - Saurabh Sivakumar
- Chemical
Engineering Department, Carnegie Mellon
University, Pittsburgh, Pennsylvania 15217, United States
| | - Xiaoxiao Wang
- Chemical
Engineering Department, Carnegie Mellon
University, Pittsburgh, Pennsylvania 15217, United States
| | - Zachary W. Ulissi
- Chemical
Engineering Department, Carnegie Mellon
University, Pittsburgh, Pennsylvania 15217, United States
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42
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Botella R, Kistanov AA, Cao W. Swarm Smart Meta-Estimator for 2D/2D Heterostructure Design. J Chem Inf Model 2023; 63:6212-6223. [PMID: 37796976 PMCID: PMC10598791 DOI: 10.1021/acs.jcim.3c01509] [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: 09/19/2023] [Indexed: 10/07/2023]
Abstract
Two-dimensional (2D) semiconductors are central to many scientific fields. The combination of two semiconductors (heterostructure) is a good way to lift many technological deadlocks. Although ab initio calculations are useful to study physical properties of these composites, their application is limited to few heterostructure samples. Herein, we use machine learning to predict key characteristics of 2D materials to select relevant candidates for heterostructure building. First, a label space is created with engineered labels relating to atomic charge and ion spatial distribution. Then, a meta-estimator is designed to predict label values of heterostructure samples having a defined band alignment (descriptor). To this end, independently trained k-nearest neighbors (KNN) regression models are combined to boost the regression. Then, swarm intelligence principles are used, along with the boosted estimator's results, to further refine the regression. This new "swarm smart" algorithm is a powerful and versatile tool to select, among experimentally existing, computationally studied, and not yet discovered van der Waals heterostructures, the most likely candidate materials to face the scientific challenges ahead.
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Affiliation(s)
- Romain Botella
- Nano and Molecular Systems Research
Unit, Faculty of Science, University of
Oulu, FIN 90014 Oulu, Finland
| | - Andrey A. Kistanov
- Nano and Molecular Systems Research
Unit, Faculty of Science, University of
Oulu, FIN 90014 Oulu, Finland
| | - Wei Cao
- Nano and Molecular Systems Research
Unit, Faculty of Science, University of
Oulu, FIN 90014 Oulu, Finland
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43
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Aziz MT, Naqvi SAR, Janjua MRSA, Alam M, Gill WA. Exploring the adsorption behavior of molecular hydrogen on CHA-zeolite by comparing the performance of various force field methods. RSC Adv 2023; 13:30937-30950. [PMID: 37876651 PMCID: PMC10591995 DOI: 10.1039/d3ra04262f] [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: 06/25/2023] [Accepted: 10/12/2023] [Indexed: 10/26/2023] Open
Abstract
Molecular hydrogen (H2) adsorption plays a crucial role in numerous applications, including hydrogen storage and purification processes. Understanding the interaction of H2 with porous materials is essential for designing efficient adsorption systems. In this study, we investigate H2 adsorption on CHA-zeolite using a combination of density functional theory (DFT) and force field-based molecular dynamics (MD) simulations. Firstly, we employ DFT calculations to explore the energetic properties and adsorption sites of H2 on the CHA-zeolite framework. The electronic structure and binding energies of H2 in various adsorption configurations are analyzed, providing valuable insights into the nature of the adsorption process. Subsequently, force field methods are employed to perform extensive MD simulations, allowing us to study the dynamic behavior of H2 molecules adsorbed on the CHA-zeolite surface. The trajectory analysis provides information on the diffusion mechanisms and mobility of H2 within the porous structure, shedding light on the transport properties of the adsorbed gas. Furthermore, the combination of DFT and MD results enables us to validate and refine the force field parameters used in simulations, improving the accuracy of the model, and enhancing our understanding of the H2-CHA interactions. Our comprehensive investigation into molecular hydrogen adsorption on CHA-zeolite using density functional theory and molecular dynamics simulations yields valuable insights into the fundamental aspects of the adsorption process. These findings contribute to the development of advanced hydrogen storage and separation technologies, paving the way for efficient and sustainable energy applications.
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Affiliation(s)
- Muhammad Tariq Aziz
- Department of Chemistry, Government College University Faisalabad Faisalabad 38000 Pakistan
| | - Syed Ali Raza Naqvi
- Department of Chemistry, Government College University Faisalabad Faisalabad 38000 Pakistan
| | | | - Manawwer Alam
- Department of Chemistry, College of Science, King Saud University Riyadh 11451 Saudi Arabia
| | - Waqas Amber Gill
- Departamento de Química Física, Universidad de Valencia Avda Dr Moliner, 50, E-46100 Burjassot Valencia Spain
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44
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Zhang Y, Jiang B. Universal machine learning for the response of atomistic systems to external fields. Nat Commun 2023; 14:6424. [PMID: 37827998 PMCID: PMC10570356 DOI: 10.1038/s41467-023-42148-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2023] [Accepted: 10/01/2023] [Indexed: 10/14/2023] Open
Abstract
Machine learned interatomic interaction potentials have enabled efficient and accurate molecular simulations of closed systems. However, external fields, which can greatly change the chemical structure and/or reactivity, have been seldom included in current machine learning models. This work proposes a universal field-induced recursively embedded atom neural network (FIREANN) model, which integrates a pseudo field vector-dependent feature into atomic descriptors to represent system-field interactions with rigorous rotational equivariance. This "all-in-one" approach correlates various response properties like dipole moment and polarizability with the field-dependent potential energy in a single model, very suitable for spectroscopic and dynamics simulations in molecular and periodic systems in the presence of electric fields. Especially for periodic systems, we find that FIREANN can overcome the intrinsic multiple-value issue of the polarization by training atomic forces only. These results validate the universality and capability of the FIREANN method for efficient first-principles modeling of complicated systems in strong external fields.
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Affiliation(s)
- Yaolong Zhang
- Key Laboratory of Precision and Intelligent Chemistry, Department of Chemical Physics, Key Laboratory of Surface and Interface Chemistry and Energy Catalysis of Anhui Higher Education Institutes, University of Science and Technology of China, Hefei, Anhui, 230026, China
- École Polytechnique Fédérale de Lausanne, 1015, Lausanne, Switzerland
| | - Bin Jiang
- Key Laboratory of Precision and Intelligent Chemistry, Department of Chemical Physics, Key Laboratory of Surface and Interface Chemistry and Energy Catalysis of Anhui Higher Education Institutes, University of Science and Technology of China, Hefei, Anhui, 230026, China.
- Hefei National Laboratory, University of Science and Technology of China, Hefei, 230088, China.
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45
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Stocker S, Jung H, Csányi G, Goldsmith CF, Reuter K, Margraf JT. Estimating Free Energy Barriers for Heterogeneous Catalytic Reactions with Machine Learning Potentials and Umbrella Integration. J Chem Theory Comput 2023; 19:6796-6804. [PMID: 37747812 PMCID: PMC10569033 DOI: 10.1021/acs.jctc.3c00541] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Indexed: 09/27/2023]
Abstract
Predicting the rate constants of elementary reaction steps is key for the computational modeling of catalytic processes. Within transition state theory (TST), this requires an accurate estimation of the corresponding free energy barriers. While sophisticated methods for estimating free energy differences exist, these typically require extensive (biased) molecular dynamics simulations that are computationally prohibitive with the first-principles electronic structure methods that are typically used in catalysis research. In this contribution, we show that machine-learning (ML) interatomic potentials can be trained in an automated iterative workflow to perform such free energy calculations at a much reduced computational cost as compared to a direct density functional theory (DFT) based evaluation. For the decomposition of CHO on Rh(111), we find that thermal effects are substantial and lead to a decrease in the free energy barrier, which can be vanishingly small, depending on the DFT functional used. This is in stark contrast to previously reported estimates based on a harmonic TST approximation, which predicted an increase in the barrier at elevated temperatures. Since CHO is the reactant of the putative rate limiting reaction step in syngas conversion on Rh(111) and essential for the selectivity toward oxygenates containing multiple carbon atoms (C2+ oxygenates), our results call into question the reported mechanism established by microkinetic models.
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Affiliation(s)
- Sina Stocker
- Fritz-Haber-Institut
der Max-Planck-Gesellschaft, Faradayweg 4-6, 14195 Berlin, Germany
| | - Hyunwook Jung
- Fritz-Haber-Institut
der Max-Planck-Gesellschaft, Faradayweg 4-6, 14195 Berlin, Germany
| | - Gábor Csányi
- Engineering
Laboratory, University of Cambridge, Cambridge CB2 1PZ, United Kingdom
| | - C. Franklin Goldsmith
- Fritz-Haber-Institut
der Max-Planck-Gesellschaft, Faradayweg 4-6, 14195 Berlin, Germany
- School
of Engineering, Brown University, Providence, Rhode Island 02912, United States
| | - Karsten Reuter
- Fritz-Haber-Institut
der Max-Planck-Gesellschaft, Faradayweg 4-6, 14195 Berlin, Germany
| | - Johannes T. Margraf
- Fritz-Haber-Institut
der Max-Planck-Gesellschaft, Faradayweg 4-6, 14195 Berlin, Germany
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46
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Lehner MT, Katzberger P, Maeder N, Schiebroek CC, Teetz J, Landrum GA, Riniker S. DASH: Dynamic Attention-Based Substructure Hierarchy for Partial Charge Assignment. J Chem Inf Model 2023; 63:6014-6028. [PMID: 37738206 PMCID: PMC10565818 DOI: 10.1021/acs.jcim.3c00800] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Indexed: 09/24/2023]
Abstract
We present a robust and computationally efficient approach for assigning partial charges of atoms in molecules. The method is based on a hierarchical tree constructed from attention values extracted from a graph neural network (GNN), which was trained to predict atomic partial charges from accurate quantum-mechanical (QM) calculations. The resulting dynamic attention-based substructure hierarchy (DASH) approach provides fast assignment of partial charges with the same accuracy as the GNN itself, is software-independent, and can easily be integrated in existing parametrization pipelines, as shown for the Open force field (OpenFF). The implementation of the DASH workflow, the final DASH tree, and the training set are available as open source/open data from public repositories.
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Affiliation(s)
| | | | - Niels Maeder
- Department of Chemistry and
Applied Biosciences, ETH Zurich, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
| | - Carl C.G. Schiebroek
- Department of Chemistry and
Applied Biosciences, ETH Zurich, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
| | - Jakob Teetz
- Department of Chemistry and
Applied Biosciences, ETH Zurich, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
| | - Gregory A. Landrum
- Department of Chemistry and
Applied Biosciences, ETH Zurich, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
| | - Sereina Riniker
- Department of Chemistry and
Applied Biosciences, ETH Zurich, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
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47
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Simkó I, Schran C, Brieuc F, Fábri C, Asvany O, Schlemmer S, Marx D, Császár AG. Quantum Nuclear Delocalization and its Rovibrational Fingerprints. Angew Chem Int Ed Engl 2023; 62:e202306744. [PMID: 37561837 DOI: 10.1002/anie.202306744] [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: 05/15/2023] [Revised: 08/07/2023] [Accepted: 08/10/2023] [Indexed: 08/12/2023]
Abstract
Quantum mechanics dictates that nuclei must undergo some delocalization. In this work, emergence of quantum nuclear delocalization and its rovibrational fingerprints are discussed for the case of the van der Waals complexHHe 3 + ${{\rm{HHe}}_3^ + }$ . The equilibrium structure ofHHe 3 + ${{\rm{HHe}}_3^ + }$ is planar and T-shaped, one He atom solvating the quasi-linear He-H+ -He core. The dynamical structure ofHHe 3 + ${{\rm{HHe}}_3^ + }$ , in all of its bound states, is fundamentally different. As revealed by spatial distribution functions and nuclear densities, during the vibrations of the molecule the solvating He is not restricted to be in the plane defined by the instantaneously bentHHe 2 + ${{\rm{HHe}}_2^ + }$ chomophore, but freely orbits the central proton, forming a three-dimensional torus around theHHe 2 + ${{\rm{HHe}}_2^ + }$ chromophore. This quantum delocalization is observed for all vibrational states, the type of vibrational excitation being reflected in the topology of the nodal surfaces in the nuclear densities, showing, for example, that intramolecular bending involves excitation along the circumference of the torus.
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Affiliation(s)
- Irén Simkó
- Laboratory of Molecular Structure and Dynamics, Institute of Chemistry, ELTE Eötvös Loránd University, H-1117, Budapest, Pázmány Péter sétány 1/A, Hungary
- MTA-ELTE Complex Chemical Systems Research Group, H-1117, Budapest, Pázmány Péter sétány 1/A, Hungary
- Hevesy György PhD School of Chemistry, ELTE Eötvös Loránd University, H-1117, Budapest, Pázmány Péter sétány 1/A, Hungary
| | - Christoph Schran
- Lehrstuhl für Theoretische Chemie, Ruhr-Universität Bochum, 44780, Bochum, Germany
- Present address: Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK
| | - Fabien Brieuc
- Lehrstuhl für Theoretische Chemie, Ruhr-Universität Bochum, 44780, Bochum, Germany
- Present address: Laboratoire Matière en Conditions Extrêmes, Université Paris-Saclay, CEA, DAM, DIF, 91297, Arpajon, France
| | - Csaba Fábri
- MTA-ELTE Complex Chemical Systems Research Group, H-1117, Budapest, Pázmány Péter sétány 1/A, Hungary
| | - Oskar Asvany
- I. Physikalisches Institut, Universität zu Köln, Zülpicher Str. 77, 50937, Köln, Germany
| | - Stephan Schlemmer
- I. Physikalisches Institut, Universität zu Köln, Zülpicher Str. 77, 50937, Köln, Germany
| | - Dominik Marx
- Lehrstuhl für Theoretische Chemie, Ruhr-Universität Bochum, 44780, Bochum, Germany
| | - Attila G Császár
- Laboratory of Molecular Structure and Dynamics, Institute of Chemistry, ELTE Eötvös Loránd University, H-1117, Budapest, Pázmány Péter sétány 1/A, Hungary
- MTA-ELTE Complex Chemical Systems Research Group, H-1117, Budapest, Pázmány Péter sétány 1/A, Hungary
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48
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Hermann J, Spencer J, Choo K, Mezzacapo A, Foulkes WMC, Pfau D, Carleo G, Noé F. Ab initio quantum chemistry with neural-network wavefunctions. Nat Rev Chem 2023; 7:692-709. [PMID: 37558761 DOI: 10.1038/s41570-023-00516-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/16/2023] [Indexed: 08/11/2023]
Abstract
Deep learning methods outperform human capabilities in pattern recognition and data processing problems and now have an increasingly important role in scientific discovery. A key application of machine learning in molecular science is to learn potential energy surfaces or force fields from ab initio solutions of the electronic Schrödinger equation using data sets obtained with density functional theory, coupled cluster or other quantum chemistry (QC) methods. In this Review, we discuss a complementary approach using machine learning to aid the direct solution of QC problems from first principles. Specifically, we focus on quantum Monte Carlo methods that use neural-network ansatzes to solve the electronic Schrödinger equation, in first and second quantization, computing ground and excited states and generalizing over multiple nuclear configurations. Although still at their infancy, these methods can already generate virtually exact solutions of the electronic Schrödinger equation for small systems and rival advanced conventional QC methods for systems with up to a few dozen electrons.
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Affiliation(s)
- Jan Hermann
- Microsoft Research AI4Science, Berlin, Germany
- FU Berlin, Department of Mathematics and Computer Science, Berlin, Germany
| | | | - Kenny Choo
- Department of Physics, University of Zurich, Zurich, Switzerland
- IBM Quantum, IBM Research Zurich, Ruschlikon, Switzerland
| | | | - W M C Foulkes
- Imperial College London, Department of Physics, London, UK
| | - David Pfau
- DeepMind, London, UK.
- Imperial College London, Department of Physics, London, UK.
| | | | - Frank Noé
- Microsoft Research AI4Science, Berlin, Germany.
- FU Berlin, Department of Mathematics and Computer Science, Berlin, Germany.
- FU Berlin, Department of Physics, Berlin, Germany.
- Department of Chemistry,Rice University, Houston, TX, USA.
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49
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Tokita AM, Behler J. How to train a neural network potential. J Chem Phys 2023; 159:121501. [PMID: 38127396 DOI: 10.1063/5.0160326] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Accepted: 07/24/2023] [Indexed: 12/23/2023] Open
Abstract
The introduction of modern Machine Learning Potentials (MLPs) has led to a paradigm change in the development of potential energy surfaces for atomistic simulations. By providing efficient access to energies and forces, they allow us to perform large-scale simulations of extended systems, which are not directly accessible by demanding first-principles methods. In these simulations, MLPs can reach the accuracy of electronic structure calculations, provided that they have been properly trained and validated using a suitable set of reference data. Due to their highly flexible functional form, the construction of MLPs has to be done with great care. In this Tutorial, we describe the necessary key steps for training reliable MLPs, from data generation via training to final validation. The procedure, which is illustrated for the example of a high-dimensional neural network potential, is general and applicable to many types of MLPs.
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Affiliation(s)
- Alea Miako Tokita
- Lehrstuhl für Theoretische Chemie II, Ruhr-Universität Bochum, 44780 Bochum, Germany and Research Center Chemical Sciences and Sustainability, Research Alliance Ruhr, 44780 Bochum, Germany
| | - Jörg Behler
- Lehrstuhl für Theoretische Chemie II, Ruhr-Universität Bochum, 44780 Bochum, Germany and Research Center Chemical Sciences and Sustainability, Research Alliance Ruhr, 44780 Bochum, Germany
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50
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Nguyen MT, Fernandez CA, Haider MM, Chu KH, Jian G, Nassiri S, Zhang D, Rousseau R, Glezakou VA. Toward Self-Healing Concrete Infrastructure: Review of Experiments and Simulations across Scales. Chem Rev 2023; 123:10838-10876. [PMID: 37286529 DOI: 10.1021/acs.chemrev.2c00709] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Cement and concrete are vital materials used to construct durable habitats and infrastructure that withstand natural and human-caused disasters. Still, concrete cracking imposes enormous repair costs on societies, and excessive cement consumption for repairs contributes to climate change. Therefore, the need for more durable cementitious materials, such as those with self-healing capabilities, has become more urgent. In this review, we present the functioning mechanisms of five different strategies for implementing self-healing capability into cement based materials: (1) autogenous self-healing from ordinary portland cement and supplementary cementitious materials and geopolymers in which defects and cracks are repaired through intrinsic carbonation and crystallization; (2) autonomous self-healing by (a) biomineralization wherein bacteria within the cement produce carbonates, silicates, or phosphates to heal damage, (b) polymer-cement composites in which autonomous self-healing occurs both within the polymer and at the polymer-cement interface, and (c) fibers that inhibit crack propagation, thus allowing autogenous healing mechanisms to be more effective. In all cases, we discuss the self-healing agent and synthesize the state of knowledge on the self-healing mechanism(s). In this review article, the state of computational modeling across nano- to macroscales developed based on experimental data is presented for each self-healing approach. We conclude the review by noting that, although autogenous reactions help repair small cracks, the most fruitful opportunities lay within design strategies for additional components that can migrate into cracks and initiate chemistries that retard crack propagation and generate repair of the cement matrix.
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Affiliation(s)
| | | | - Md Mostofa Haider
- University of California, Davis, One Shield Avenue, Davis, California 95616, USA
| | - Kung-Hui Chu
- Zachry Department of Civil and Environmental Engineering, Texas A&M University, College Station, Texas 77843, USA
| | - Guoqing Jian
- Pacific Northwest National Laboratory, Richland, Washington 99352, USA
| | - Somayeh Nassiri
- University of California, Davis, One Shield Avenue, Davis, California 95616, USA
| | - Difan Zhang
- Pacific Northwest National Laboratory, Richland, Washington 99352, USA
| | - Roger Rousseau
- Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, USA
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