1
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Ma H, Pan SQ, Wang WL, Yue X, Xi XH, Yan S, Wu DY, Wang X, Liu G, Ren B. Surface-Enhanced Raman Spectroscopy: Current Understanding, Challenges, and Opportunities. ACS NANO 2024. [PMID: 38764194 DOI: 10.1021/acsnano.4c02670] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2024]
Abstract
While surface-enhanced Raman spectroscopy (SERS) has experienced substantial advancements since its discovery in the 1970s, it is an opportunity to celebrate achievements, consider ongoing endeavors, and anticipate the future trajectory of SERS. In this perspective, we encapsulate the latest breakthroughs in comprehending the electromagnetic enhancement mechanisms of SERS, and revisit CT mechanisms of semiconductors. We then summarize the strategies to improve sensitivity, selectivity, and reliability. After addressing experimental advancements, we comprehensively survey the progress on spectrum-structure correlation of SERS showcasing their important role in promoting SERS development. Finally, we anticipate forthcoming directions and opportunities, especially in deepening our insights into chemical or biological processes and establishing a clear spectrum-structure correlation.
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Affiliation(s)
- Hao Ma
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (i-ChEM), College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
| | - Si-Qi Pan
- State Key Laboratory of Marine Environmental Science, College of the Environment and Ecology, Fujian Provincial Key Laboratory for Coastal Ecology and Environmental Studies, Center for Marine Environmental Chemistry & Toxicology, Xiamen University, Xiamen 361102, China
| | - Wei-Li Wang
- State Key Laboratory of Marine Environmental Science, College of the Environment and Ecology, Fujian Provincial Key Laboratory for Coastal Ecology and Environmental Studies, Center for Marine Environmental Chemistry & Toxicology, Xiamen University, Xiamen 361102, China
| | - Xiaxia Yue
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (i-ChEM), College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
| | - Xiao-Han Xi
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (i-ChEM), College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
| | - Sen Yan
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (i-ChEM), College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
| | - De-Yin Wu
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (i-ChEM), College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
| | - Xiang Wang
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (i-ChEM), College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
| | - Guokun Liu
- State Key Laboratory of Marine Environmental Science, College of the Environment and Ecology, Fujian Provincial Key Laboratory for Coastal Ecology and Environmental Studies, Center for Marine Environmental Chemistry & Toxicology, Xiamen University, Xiamen 361102, China
| | - Bin Ren
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (i-ChEM), College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
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2
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Chen SM, Zhang ZB, Gao HL, Yu SH. Bottom-Up Film-to-Bulk Assembly Toward Bioinspired Bulk Structural Nanocomposites. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024:e2313443. [PMID: 38414173 DOI: 10.1002/adma.202313443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/10/2023] [Revised: 02/21/2024] [Indexed: 02/29/2024]
Abstract
Biological materials, although composed of meager minerals and biopolymers, often exhibit amazing mechanical properties far beyond their components due to hierarchically ordered structures. Understanding their structure-properties relationships and replicating them into artificial materials would boost the development of bulk structural nanocomposites. Layered microstructure widely exists in biological materials, serving as the fundamental structure in nanosheet-based nacres and nanofiber-based Bouligand tissues, and implying superior mechanical properties. High-efficient and scalable fabrication of bioinspired bulk structural nanocomposites with precise layered microstructure is therefore important yet remains difficult. Here, one straightforward bottom-up film-to-bulk assembly strategy is focused for fabricating bioinspired layered bulk structural nanocomposites. The bottom-up assembly strategy inherently offers a methodology for precise construction of bioinspired layered microstructure in bulk form, availability for fabrication of bioinspired bulk structural nanocomposites with large sizes and complex shapes, possibility for design of multiscale interfaces, feasibility for manipulation of diverse heterogeneities. Not limited to discussing what has been achieved by using the current bottom-up film-to-bulk assembly strategy, it is also envisioned how to promote such an assembly strategy to better benefit the development of bioinspired bulk structural nanocomposites. Compared to other assembly strategies, the highlighted strategy provides great opportunities for creating bioinspired bulk structural nanocomposites on demand.
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Affiliation(s)
- Si-Ming Chen
- Department of Chemistry, New Cornerstone Science Laboratory, Institute of Biomimetic Materials & Chemistry, Anhui Engineering Laboratory of Biomimetic Materials, Division of Nanomaterials & Chemistry, Hefei National Research Center for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei, 230026, China
| | - Zhen-Bang Zhang
- Department of Chemistry, Department of Materials Science and Engineering, Institute of Innovative Materials, Shenzhen Key Laboratory of Sustainable Biomimetic Materials, Guangming Advanced Research Institute, Southern University of Science and Technology, Shenzhen, 518055, China
| | - Huai-Ling Gao
- Department of Chemistry, New Cornerstone Science Laboratory, Institute of Biomimetic Materials & Chemistry, Anhui Engineering Laboratory of Biomimetic Materials, Division of Nanomaterials & Chemistry, Hefei National Research Center for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei, 230026, China
- CAS Key Laboratory of Mechanical Behavior and Design of Materials, Department of Modern Mechanics, University of Science and Technology of China, Hefei, 230027, China
| | - Shu-Hong Yu
- Department of Chemistry, New Cornerstone Science Laboratory, Institute of Biomimetic Materials & Chemistry, Anhui Engineering Laboratory of Biomimetic Materials, Division of Nanomaterials & Chemistry, Hefei National Research Center for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei, 230026, China
- Department of Chemistry, Department of Materials Science and Engineering, Institute of Innovative Materials, Shenzhen Key Laboratory of Sustainable Biomimetic Materials, Guangming Advanced Research Institute, Southern University of Science and Technology, Shenzhen, 518055, China
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3
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Bai J, Mosbach S, Taylor CJ, Karan D, Lee KF, Rihm SD, Akroyd J, Lapkin AA, Kraft M. A dynamic knowledge graph approach to distributed self-driving laboratories. Nat Commun 2024; 15:462. [PMID: 38263405 PMCID: PMC10805810 DOI: 10.1038/s41467-023-44599-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 12/21/2023] [Indexed: 01/25/2024] Open
Abstract
The ability to integrate resources and share knowledge across organisations empowers scientists to expedite the scientific discovery process. This is especially crucial in addressing emerging global challenges that require global solutions. In this work, we develop an architecture for distributed self-driving laboratories within The World Avatar project, which seeks to create an all-encompassing digital twin based on a dynamic knowledge graph. We employ ontologies to capture data and material flows in design-make-test-analyse cycles, utilising autonomous agents as executable knowledge components to carry out the experimentation workflow. Data provenance is recorded to ensure its findability, accessibility, interoperability, and reusability. We demonstrate the practical application of our framework by linking two robots in Cambridge and Singapore for a collaborative closed-loop optimisation for a pharmaceutically-relevant aldol condensation reaction in real-time. The knowledge graph autonomously evolves toward the scientist's research goals, with the two robots effectively generating a Pareto front for cost-yield optimisation in three days.
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Affiliation(s)
- Jiaru Bai
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge, CB3 0AS, UK
| | - Sebastian Mosbach
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge, CB3 0AS, UK
- Cambridge Centre for Advanced Research and Education in Singapore (CARES), 1 Create Way, CREATE Tower, #05-05, Singapore, 138602, Singapore
| | - Connor J Taylor
- Astex Pharmaceuticals, 436 Cambridge Science Park Milton Road, Cambridge, CB4 0QA, UK
- Innovation Centre in Digital Molecular Technologies, Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK
- Faculty of Engineering, University of Nottingham, University Park, Nottingham, NG7 2RD, UK
| | - Dogancan Karan
- Cambridge Centre for Advanced Research and Education in Singapore (CARES), 1 Create Way, CREATE Tower, #05-05, Singapore, 138602, Singapore
| | - Kok Foong Lee
- CMCL Innovations, Sheraton House, Cambridge, CB3 0AX, UK
| | - Simon D Rihm
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge, CB3 0AS, UK
- Cambridge Centre for Advanced Research and Education in Singapore (CARES), 1 Create Way, CREATE Tower, #05-05, Singapore, 138602, Singapore
| | - Jethro Akroyd
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge, CB3 0AS, UK
- Cambridge Centre for Advanced Research and Education in Singapore (CARES), 1 Create Way, CREATE Tower, #05-05, Singapore, 138602, Singapore
| | - Alexei A Lapkin
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge, CB3 0AS, UK
- Cambridge Centre for Advanced Research and Education in Singapore (CARES), 1 Create Way, CREATE Tower, #05-05, Singapore, 138602, Singapore
- Innovation Centre in Digital Molecular Technologies, Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK
| | - Markus Kraft
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge, CB3 0AS, UK.
- Cambridge Centre for Advanced Research and Education in Singapore (CARES), 1 Create Way, CREATE Tower, #05-05, Singapore, 138602, Singapore.
- School of Chemical and Biomedical Engineering, Nanyang Technological University, 62 Nanyang Drive, 637459, Singapore, Singapore.
- The Alan Turing Institute, London, NW1 2DB, UK.
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4
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Zhang B, Xiao H, Ye G, Song Z, Han T, Sharman E, Luo M, Cheng A, Zhu Q, Zhao H, Zhang G, Wang S, Jiang J. Label-Free Data Mining of Scientific Literature by Unsupervised Syntactic Distance Analysis. J Phys Chem Lett 2024; 15:212-219. [PMID: 38157213 DOI: 10.1021/acs.jpclett.3c03345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2024]
Abstract
Label-free data mining can efficiently feed large amounts of data from the vast scientific literature into artificial intelligence (AI) processing systems. Here, we demonstrate an unsupervised syntactic distance analysis (SDA) approach that is capable of mining chemical substances, functions, properties, and operations without annotation. This SDA approach was evaluated in several areas of research from the physical sciences and achieved performance in information mining comparable to that of supervised learning, as shown by its satisfactory scores of 0.62-0.72, 0.60-0.82, and 0.86-0.95 in precision, recall, and accuracy, respectively. We also showcase how our approach can assist robotic chemists programmed to perform research focused on double-perovskite colloidal nanocrystals, gold colloidal nanocrystals, oxygen evolution reaction catalysts, and enzyme-like catalysts by designing materials, formulations, and synthesis parameters based on data mined from 1.1 million literature references.
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Affiliation(s)
- Baicheng Zhang
- Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Hengyu Xiao
- Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Guilin Ye
- Hefei JiShu Quantum Technology Co. Ltd., Hefei 230026, China
| | - Zhaokun Song
- Hefei JiShu Quantum Technology Co. Ltd., Hefei 230026, China
| | - Tiantian Han
- Hefei JiShu Quantum Technology Co. Ltd., Hefei 230026, China
| | - Edward Sharman
- Department of Neurology, University of California, Irvine, California 92697, United States
| | - Man Luo
- Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Aoyuan Cheng
- Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Qing Zhu
- Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Haitao Zhao
- Materials Interfaces Center, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Guoqing Zhang
- Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Song Wang
- Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Jun Jiang
- Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, Anhui 230026, China
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5
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Qiu H, Liu L, Qiu X, Dai X, Ji X, Sun ZY. PolyNC: a natural and chemical language model for the prediction of unified polymer properties. Chem Sci 2024; 15:534-544. [PMID: 38179518 PMCID: PMC10763023 DOI: 10.1039/d3sc05079c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Accepted: 12/04/2023] [Indexed: 01/06/2024] Open
Abstract
Language models exhibit a profound aptitude for addressing multimodal and multidomain challenges, a competency that eludes the majority of off-the-shelf machine learning models. Consequently, language models hold great potential for comprehending the intricate interplay between material compositions and diverse properties, thereby accelerating material design, particularly in the realm of polymers. While past limitations in polymer data hindered the use of data-intensive language models, the growing availability of standardized polymer data and effective data augmentation techniques now opens doors to previously uncharted territories. Here, we present a revolutionary model to enable rapid and precise prediction of Polymer properties via the power of Natural language and Chemical language (PolyNC). To showcase the efficacy of PolyNC, we have meticulously curated a labeled prompt-structure-property corpus encompassing 22 970 polymer data points on a series of essential polymer properties. Through the use of natural language prompts, PolyNC gains a comprehensive understanding of polymer properties, while employing chemical language (SMILES) to describe polymer structures. In a unified text-to-text manner, PolyNC consistently demonstrates exceptional performance on both regression tasks (such as property prediction) and the classification task (polymer classification). Simultaneous and interactive multitask learning enables PolyNC to holistically grasp the structure-property relationships of polymers. Through a combination of experiments and characterizations, the generalization ability of PolyNC has been demonstrated, with attention analysis further indicating that PolyNC effectively learns structural information about polymers from multimodal inputs. This work provides compelling evidence of the potential for deploying end-to-end language models in polymer research, representing a significant advancement in the AI community's dedicated pursuit of advancing polymer science.
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Affiliation(s)
- Haoke Qiu
- State Key Laboratory of Polymer Physics and Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences Changchun 130022 China
- School of Applied Chemistry and Engineering, University of Science and Technology of China Hefei 230026 China
| | - Lunyang Liu
- State Key Laboratory of Polymer Physics and Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences Changchun 130022 China
| | - Xuepeng Qiu
- School of Applied Chemistry and Engineering, University of Science and Technology of China Hefei 230026 China
- CAS Key Laboratory of High-Performance Synthetic Rubber and its Composite Materials, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences Changchun 130022 China
| | - Xuemin Dai
- CAS Key Laboratory of High-Performance Synthetic Rubber and its Composite Materials, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences Changchun 130022 China
| | - Xiangling Ji
- State Key Laboratory of Polymer Physics and Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences Changchun 130022 China
- School of Applied Chemistry and Engineering, University of Science and Technology of China Hefei 230026 China
| | - Zhao-Yan Sun
- State Key Laboratory of Polymer Physics and Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences Changchun 130022 China
- School of Applied Chemistry and Engineering, University of Science and Technology of China Hefei 230026 China
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6
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Mou J, Ding J, Qin W. Modern Potentiometric Biosensing Based on Non-Equilibrium Measurement Techniques. Chemistry 2023; 29:e202302647. [PMID: 37733874 DOI: 10.1002/chem.202302647] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 09/20/2023] [Accepted: 09/21/2023] [Indexed: 09/23/2023]
Abstract
Modern potentiometric sensors based on polymeric membrane ion-selective electrodes (ISEs) have achieved new breakthroughs in sensitivity, selectivity, and stability and have extended applications in environmental surveillance, medical diagnostics, and industrial analysis. Moreover, nonclassical potentiometry shows promise for many applications and opens up new opportunities for potentiometric biosensing. Here, we aim to provide a concept to summarize advances over the past decade in the development of potentiometric biosensors with polymeric membrane ISEs. This Concept article articulates sensing mechanisms based on non-equilibrium measurement techniques. In particular, we emphasize new trends in potentiometric biosensing based on attractive dynamic approaches. Representative examples are selected to illustrate key applications under zero-current conditions and stimulus-controlled modes. More importantly, fruitful information obtained from non-equilibrium measurements with dynamic responses can be useful for artificial intelligence (AI). The combination of ISEs with advanced AI techniques for effective data processing is also discussed. We hope that this Concept will illustrate the great possibilities offered by non-equilibrium measurement techniques and AI in potentiometric biosensing and encourage further innovations in this exciting field.
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Affiliation(s)
- Junsong Mou
- CAS Key Laboratory of Coastal Environmental Processes, and Ecological Remediation, Shandong Key Laboratory of Coastal Environmental Processes, YICCAS, Yantai Institute of Coastal Zone Research (YIC), Chinese Academy of Sciences (CAS), Yantai, 264003, Shandong, P. R. China
- University of Chinese Academy of Sciences, Beijing, 100049, P. R. China
| | - Jiawang Ding
- CAS Key Laboratory of Coastal Environmental Processes, and Ecological Remediation, Shandong Key Laboratory of Coastal Environmental Processes, YICCAS, Yantai Institute of Coastal Zone Research (YIC), Chinese Academy of Sciences (CAS), Yantai, 264003, Shandong, P. R. China
- Qingdao National Laboratory for Marine Science and Technology, Qingdao 266237, Shandong (P. R. China), Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao, 266071, Shandong, P. R. China
| | - Wei Qin
- CAS Key Laboratory of Coastal Environmental Processes, and Ecological Remediation, Shandong Key Laboratory of Coastal Environmental Processes, YICCAS, Yantai Institute of Coastal Zone Research (YIC), Chinese Academy of Sciences (CAS), Yantai, 264003, Shandong, P. R. China
- Qingdao National Laboratory for Marine Science and Technology, Qingdao 266237, Shandong (P. R. China), Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao, 266071, Shandong, P. R. China
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7
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Feng S, Cai A, Wang Y, Zhang B, Qiao Q, Chen C, Wang S, Jiang J. A robotic AI-Chemist system for multi-modal AI-ready database. Natl Sci Rev 2023; 10:nwad332. [PMID: 38226367 PMCID: PMC10789233 DOI: 10.1093/nsr/nwad332] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2023] [Revised: 10/16/2023] [Accepted: 10/31/2023] [Indexed: 01/17/2024] Open
Abstract
By fusing literature data mining, high-performance simulations, and high-accuracy experiments, robotic AI-Chemist can achieve automated high-throughput production, classification, cleaning, association and fusion of data, and thus develop a multi-modal AI-ready database.
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Affiliation(s)
- Shuo Feng
- Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, China
| | - Aoran Cai
- Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, China
| | - Yang Wang
- Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, China
| | - Baicheng Zhang
- Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, China
| | - Qinyu Qiao
- Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, China
| | - Cheng Chen
- Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, China
| | - Song Wang
- Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, China
| | - Jun Jiang
- Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, China
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8
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Xie Y, Feng S, Deng L, Cai A, Gan L, Jiang Z, Yang P, Ye G, Liu Z, Wen L, Zhu Q, Zhang W, Zhang Z, Li J, Feng Z, Zhang C, Du W, Xu L, Jiang J, Chen X, Zou G. Inverse design of chiral functional films by a robotic AI-guided system. Nat Commun 2023; 14:6177. [PMID: 37794036 PMCID: PMC10551020 DOI: 10.1038/s41467-023-41951-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2023] [Accepted: 09/18/2023] [Indexed: 10/06/2023] Open
Abstract
Artificial chiral materials and nanostructures with strong and tuneable chiroptical activities, including sign, magnitude, and wavelength distribution, are useful owing to their potential applications in chiral sensing, enantioselective catalysis, and chiroptical devices. Thus, the inverse design and customized manufacturing of these materials is highly desirable. Here, we use an artificial intelligence (AI) guided robotic chemist to accurately predict chiroptical activities from the experimental absorption spectra and structure/process parameters, and generate chiral films with targeted chiroptical activities across the full visible spectrum. The robotic AI-chemist carries out the entire process, including chiral film construction, characterization, and testing. A machine learned reverse design model using spectrum embedded descriptors is developed to predict optimal structure/process parameters for any targeted chiroptical property. A series of chiral films with a dissymmetry factor as high as 1.9 (gabs ~ 1.9) are identified out of more than 100 million possible structures, and their feasible application in circular polarization-selective color filters for multiplex laser display and switchable circularly polarized (CP) luminescence is demonstrated. Our findings not only provide chiral films with the highest reported chiroptical activity, but also have great fundamental value for the inverse design of chiroptical materials.
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Affiliation(s)
- Yifan Xie
- Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, Anhui, China
| | - Shuo Feng
- Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, Anhui, China
| | - Linxiao Deng
- State Key Laboratory of Particle Detection and Electronics, Department of Optics and Optical Engineering, University of Science and Technology of China, Hefei, Anhui, China
| | - Aoran Cai
- Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, Anhui, China
| | - Liyu Gan
- Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, Anhui, China
| | - Zifan Jiang
- Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, Anhui, China
| | - Peng Yang
- Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, Anhui, China
| | - Guilin Ye
- Hefei JiShu Quantum Technology Co. Ltd., Hefei, China
| | - Zaiqing Liu
- Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, Anhui, China
| | - Li Wen
- Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, Anhui, China
| | - Qing Zhu
- Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, Anhui, China
| | - Wanjun Zhang
- Hefei JiShu Quantum Technology Co. Ltd., Hefei, China
| | - Zhanpeng Zhang
- Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, Anhui, China
| | - Jiahe Li
- Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, Anhui, China
| | - Zeyu Feng
- Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, Anhui, China
| | - Chutian Zhang
- Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, Anhui, China
| | - Wenjie Du
- Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, Anhui, China
| | - Lixin Xu
- State Key Laboratory of Particle Detection and Electronics, Department of Optics and Optical Engineering, University of Science and Technology of China, Hefei, Anhui, China
| | - Jun Jiang
- Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, Anhui, China.
| | - Xin Chen
- Suzhou Laboratory, Jiangsu, China.
| | - Gang Zou
- Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, Anhui, China.
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9
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Zeng Z, Nie YC, Ding N, Ding QJ, Ye WT, Yang C, Sun M, E W, Zhu R, Liu Z. Transcription between human-readable synthetic descriptions and machine-executable instructions: an application of the latest pre-training technology. Chem Sci 2023; 14:9360-9373. [PMID: 37712039 PMCID: PMC10498500 DOI: 10.1039/d3sc02483k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Accepted: 08/15/2023] [Indexed: 09/16/2023] Open
Abstract
AI has been widely applied in scientific scenarios, such as robots performing chemical synthetic actions to free researchers from monotonous experimental procedures. However, there exists a gap between human-readable natural language descriptions and machine-executable instructions, of which the former are typically in numerous chemical articles, and the latter are currently compiled manually by experts. We apply the latest technology of pre-trained models and achieve automatic transcription between descriptions and instructions. We design a concise and comprehensive schema of instructions and construct an open-source human-annotated dataset consisting of 3950 description-instruction pairs, with 9.2 operations in each instruction on average. We further propose knowledgeable pre-trained transcription models enhanced by multi-grained chemical knowledge. The performance of recent popular models and products showing great capability in automatic writing (e.g., ChatGPT) has also been explored. Experiments prove that our system improves the instruction compilation efficiency of researchers by at least 42%, and can generate fluent academic paragraphs of synthetic descriptions when given instructions, showing the great potential of pre-trained models in improving human productivity.
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Affiliation(s)
- Zheni Zeng
- Department of Computer Science and Technology, Tsinghua University Beijing China
| | - Yi-Chen Nie
- College of Chemistry and Molecular Engineering, Peking University Beijing China
| | - Ning Ding
- Department of Computer Science and Technology, Tsinghua University Beijing China
| | - Qian-Jun Ding
- College of Chemistry and Molecular Engineering, Peking University Beijing China
| | - Wei-Ting Ye
- College of Chemistry and Molecular Engineering, Peking University Beijing China
| | - Cheng Yang
- School of Computer Science, Beijing University of Posts and Telecommunications Beijing China
| | - Maosong Sun
- Department of Computer Science and Technology, Tsinghua University Beijing China
| | - Weinan E
- Center for Machine Learning Research and School of Mathematical Sciences, Peking University AI for Science Institute Beijing China
| | - Rong Zhu
- College of Chemistry and Molecular Engineering, Peking University Beijing China
| | - Zhiyuan Liu
- Department of Computer Science and Technology, Tsinghua University Beijing China
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10
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Zheng F, Lu J, Zhu Z, Jiang H, Yan Y, He Y, Yuan S, Sun Q. Predicting Molecular Self-Assembly on Metal Surfaces Using Graph Neural Networks Based on Experimental Data Sets. ACS NANO 2023; 17:17545-17553. [PMID: 37611029 DOI: 10.1021/acsnano.3c06405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/25/2023]
Abstract
The application of supramolecular chemistry on solid surfaces has received extensive attention in the past few decades. To date, combining experiments with quantum mechanical or molecular dynamic methods represents the key strategy to explore the molecular self-assembled structures, which is, however, often laborious. Recently, machine learning (ML) has become one of the most exciting tools in material research, allowing for both efficiency and accuracy in predicting molecular properties. In this work, we constructed a graph neural network to predict the self-assembly of functional polycyclic aromatic hydrocarbons (PAHs) on metal surfaces. Using scanning tunneling microscopy (STM), we characterized the self-assembled nanostructures of a homologous series of PAH molecules on different metal surfaces to construct an experimental data set for model training. Compared with traditional ML algorithms, our model exhibits better predictive performance. Finally, the generalization of the model is further verified by comparing the ML predictions and experimental results of different functionalized molecule. Our results demonstrate training experimental data sets to produce a predictive ML model of molecular self-assembly with generalization performance, which allows for the predictive design of nanostructures with functional molecules.
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Affiliation(s)
- Fengru Zheng
- Materials Genome Institute, Shanghai University, 200444 Shanghai, China
| | - Jiayi Lu
- Materials Genome Institute, Shanghai University, 200444 Shanghai, China
| | - Zhiwen Zhu
- Materials Genome Institute, Shanghai University, 200444 Shanghai, China
| | - Hao Jiang
- Materials Genome Institute, Shanghai University, 200444 Shanghai, China
| | - Yuyi Yan
- Materials Genome Institute, Shanghai University, 200444 Shanghai, China
| | - Yu He
- Materials Genome Institute, Shanghai University, 200444 Shanghai, China
| | - Shaoxuan Yuan
- Materials Genome Institute, Shanghai University, 200444 Shanghai, China
| | - Qiang Sun
- Materials Genome Institute, Shanghai University, 200444 Shanghai, China
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11
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Yan X, Yue T, Winkler DA, Yin Y, Zhu H, Jiang G, Yan B. Converting Nanotoxicity Data to Information Using Artificial Intelligence and Simulation. Chem Rev 2023. [PMID: 37262026 DOI: 10.1021/acs.chemrev.3c00070] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
Decades of nanotoxicology research have generated extensive and diverse data sets. However, data is not equal to information. The question is how to extract critical information buried in vast data streams. Here we show that artificial intelligence (AI) and molecular simulation play key roles in transforming nanotoxicity data into critical information, i.e., constructing the quantitative nanostructure (physicochemical properties)-toxicity relationships, and elucidating the toxicity-related molecular mechanisms. For AI and molecular simulation to realize their full impacts in this mission, several obstacles must be overcome. These include the paucity of high-quality nanomaterials (NMs) and standardized nanotoxicity data, the lack of model-friendly databases, the scarcity of specific and universal nanodescriptors, and the inability to simulate NMs at realistic spatial and temporal scales. This review provides a comprehensive and representative, but not exhaustive, summary of the current capability gaps and tools required to fill these formidable gaps. Specifically, we discuss the applications of AI and molecular simulation, which can address the large-scale data challenge for nanotoxicology research. The need for model-friendly nanotoxicity databases, powerful nanodescriptors, new modeling approaches, molecular mechanism analysis, and design of the next-generation NMs are also critically discussed. Finally, we provide a perspective on future trends and challenges.
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Affiliation(s)
- Xiliang Yan
- Institute of Environmental Research at the Greater Bay Area, Key Laboratory for Water Quality and Conservation of the Pearl River Delta, Ministry of Education, Guangzhou University, Guangzhou 510006, China
| | - Tongtao Yue
- Key Laboratory of Marine Environment and Ecology, Ministry of Education, Institute of Coastal Environmental Pollution Control, Ocean University of China, Qingdao 266100, China
| | - David A Winkler
- Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria 3052, Australia
- School of Pharmacy, University of Nottingham, Nottingham NG7 2QL, U.K
- Department of Biochemistry and Chemistry, La Trobe Institute for Molecular Science, La Trobe University, Melbourne, Victoria 3086, Australia
| | - Yongguang Yin
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
| | - Hao Zhu
- Department of Chemistry and Biochemistry, Rowan University, Glassboro, New Jersey 08028, United States
| | - Guibin Jiang
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
| | - Bing Yan
- Institute of Environmental Research at the Greater Bay Area, Key Laboratory for Water Quality and Conservation of the Pearl River Delta, Ministry of Education, Guangzhou University, Guangzhou 510006, China
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12
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Zhang ZJ, Li SW, Oliveira JCA, Li Y, Chen X, Zhang SQ, Xu LC, Rogge T, Hong X, Ackermann L. Data-driven design of new chiral carboxylic acid for construction of indoles with C-central and C-N axial chirality via cobalt catalysis. Nat Commun 2023; 14:3149. [PMID: 37258542 DOI: 10.1038/s41467-023-38872-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Accepted: 05/16/2023] [Indexed: 06/02/2023] Open
Abstract
Challenging enantio- and diastereoselective cobalt-catalyzed C-H alkylation has been realized by an innovative data-driven knowledge transfer strategy. Harnessing the statistics of a related transformation as the knowledge source, the designed machine learning (ML) model took advantage of delta learning and enabled accurate and extrapolative enantioselectivity predictions. Powered by the knowledge transfer model, the virtual screening of a broad scope of 360 chiral carboxylic acids led to the discovery of a new catalyst featuring an intriguing furyl moiety. Further experiments verified that the predicted chiral carboxylic acid can achieve excellent stereochemical control for the target C-H alkylation, which supported the expedient synthesis for a large library of substituted indoles with C-central and C-N axial chirality. The reported machine learning approach provides a powerful data engine to accelerate the discovery of molecular catalysis by harnessing the hidden value of the available structure-performance statistics.
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Affiliation(s)
- Zi-Jing Zhang
- Institut für Organische und Biomolekulare Chemie, Georg-August-Universität Göttingen, Tammannstraße 2, 37077, Göttingen, Germany
| | - Shu-Wen Li
- Center of Chemistry for Frontier Technologies, Department of Chemistry, State Key Laboratory of Clean Energy Utilization, Zhejiang University, Hangzhou, 310027, PR China
| | - João C A Oliveira
- Institut für Organische und Biomolekulare Chemie, Georg-August-Universität Göttingen, Tammannstraße 2, 37077, Göttingen, Germany
| | - Yanjun Li
- Institut für Organische und Biomolekulare Chemie, Georg-August-Universität Göttingen, Tammannstraße 2, 37077, Göttingen, Germany
| | - Xinran Chen
- Institut für Organische und Biomolekulare Chemie, Georg-August-Universität Göttingen, Tammannstraße 2, 37077, Göttingen, Germany
- Center of Chemistry for Frontier Technologies, Department of Chemistry, State Key Laboratory of Clean Energy Utilization, Zhejiang University, Hangzhou, 310027, PR China
| | - Shuo-Qing Zhang
- Center of Chemistry for Frontier Technologies, Department of Chemistry, State Key Laboratory of Clean Energy Utilization, Zhejiang University, Hangzhou, 310027, PR China
| | - Li-Cheng Xu
- Center of Chemistry for Frontier Technologies, Department of Chemistry, State Key Laboratory of Clean Energy Utilization, Zhejiang University, Hangzhou, 310027, PR China
| | - Torben Rogge
- Institut für Organische und Biomolekulare Chemie, Georg-August-Universität Göttingen, Tammannstraße 2, 37077, Göttingen, Germany
| | - Xin Hong
- Center of Chemistry for Frontier Technologies, Department of Chemistry, State Key Laboratory of Clean Energy Utilization, Zhejiang University, Hangzhou, 310027, PR China.
- Beijing National Laboratory for Molecular Sciences, Zhongguancun North First Street No. 2, Beijing, 100190, PR China.
- Key Laboratory of Precise Synthesis of Functional Molecules of Zhejiang Province, School of Science, Westlake University, 18 Shilongshan Road, Hangzhou, 310024, Zhejiang Province, PR China.
| | - Lutz Ackermann
- Institut für Organische und Biomolekulare Chemie, Georg-August-Universität Göttingen, Tammannstraße 2, 37077, Göttingen, Germany.
- Wöhler Research Institute for Sustainable Chemistry (WISCh), Georg-August-Universität Göttingen, Tammannstraße 2, 37077, Göttingen, Germany.
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13
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Mou LH, Han T, Smith PES, Sharman E, Jiang J. Machine Learning Descriptors for Data-Driven Catalysis Study. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023:e2301020. [PMID: 37191279 PMCID: PMC10401178 DOI: 10.1002/advs.202301020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 04/07/2023] [Indexed: 05/17/2023]
Abstract
Traditional trial-and-error experiments and theoretical simulations have difficulty optimizing catalytic processes and developing new, better-performing catalysts. Machine learning (ML) provides a promising approach for accelerating catalysis research due to its powerful learning and predictive abilities. The selection of appropriate input features (descriptors) plays a decisive role in improving the predictive accuracy of ML models and uncovering the key factors that influence catalytic activity and selectivity. This review introduces tactics for the utilization and extraction of catalytic descriptors in ML-assisted experimental and theoretical research. In addition to the effectiveness and advantages of various descriptors, their limitations are also discussed. Highlighted are both 1) newly developed spectral descriptors for catalytic performance prediction and 2) a novel research paradigm combining computational and experimental ML models through suitable intermediate descriptors. Current challenges and future perspectives on the application of descriptors and ML techniques to catalysis are also presented.
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Affiliation(s)
- Li-Hui Mou
- Hefei National Research Center for Physical Sciences at the Microscale, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, Anhui, 230026, China
| | - TianTian Han
- Hefei JiShu Quantum Technology Co. Ltd., Hefei, 230026, China
| | - Pieter E S Smith
- YDS Pharmatech, ETEC, 1220 Washington Ave., Albany, NY, 12203, USA
| | - Edward Sharman
- Department of Neurology, University of California, Irvine, CA, 92697, USA
| | - Jun Jiang
- Hefei National Research Center for Physical Sciences at the Microscale, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, Anhui, 230026, China
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14
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Ge L, Ke Y, Li X. Machine learning integrated photocatalysis: progress and challenges. Chem Commun (Camb) 2023; 59:5795-5806. [PMID: 37093605 DOI: 10.1039/d3cc00989k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/25/2023]
Abstract
Discovering efficient photocatalysts has long been the goal of photocatalysis, which has traditionally been driven by serendipitous or try-and-error strategies. Recent developments in photocatalysis integrated with machine learning techniques promise to accelerate the discovery of photocatalysts, but are also facing significant challenges. In this review, advances in machine learning integrated photocatalysis are first presented from the perspective of three main photocatalytic processes: light harvesting, charge generation and separation, and surface redox reactions. Next, progress in using machine learning to understand complex photoactivity-structure relationships and identify the factors governing activity follows. A future photocatalysis paradigm is then provided with the integration of artificial intelligence, robots and automation. Lastly, we discuss the current challenges in machine learning integrated photocatalysis. This review aims to provide a systematic overview and guidelines to the broad scientific community interested in photocatalysis and artificial intelligence for solar fuel synthesis.
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Affiliation(s)
- Luyao Ge
- Key Laboratory of the Ministry of Education for Advanced Catalysis Materials, Zhejiang Key Laboratory for Reactive Chemistry on Solid Surfaces, Zhejiang Normal University, Jinhua 321004, China.
| | - Yuanzhen Ke
- Key Laboratory of the Ministry of Education for Advanced Catalysis Materials, Zhejiang Key Laboratory for Reactive Chemistry on Solid Surfaces, Zhejiang Normal University, Jinhua 321004, China.
| | - Xiaobo Li
- Key Laboratory of the Ministry of Education for Advanced Catalysis Materials, Zhejiang Key Laboratory for Reactive Chemistry on Solid Surfaces, Zhejiang Normal University, Jinhua 321004, China.
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15
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Wang G, Wu X, Xin B, Gu X, Wang G, Zhang Y, Zhao J, Cheng X, Chen C, Ma J. Machine Learning in Unmanned Systems for Chemical Synthesis. Molecules 2023; 28:molecules28052232. [PMID: 36903478 PMCID: PMC10004533 DOI: 10.3390/molecules28052232] [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: 01/15/2023] [Revised: 02/05/2023] [Accepted: 02/23/2023] [Indexed: 03/04/2023] Open
Abstract
Chemical synthesis is state-of-the-art, and, therefore, it is generally based on chemical intuition or experience of researchers. The upgraded paradigm that incorporates automation technology and machine learning (ML) algorithms has recently been merged into almost every subdiscipline of chemical science, from material discovery to catalyst/reaction design to synthetic route planning, which often takes the form of unmanned systems. The ML algorithms and their application scenarios in unmanned systems for chemical synthesis were presented. The prospects for strengthening the connection between reaction pathway exploration and the existing automatic reaction platform and solutions for improving autonomation through information extraction, robots, computer vision, and intelligent scheduling were proposed.
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Affiliation(s)
- Guoqiang Wang
- Key Laboratory of Mesoscopic Chemistry of MOE, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, China
| | - Xuefei Wu
- Department of Control Science and Intelligent Engineering, School of Management and Engineering, Nanjing University, Nanjing 210093, China
| | - Bo Xin
- Department of Control Science and Intelligent Engineering, School of Management and Engineering, Nanjing University, Nanjing 210093, China
| | - Xu Gu
- Key Laboratory of Mesoscopic Chemistry of MOE, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, China
| | - Gaobo Wang
- Key Laboratory of Mesoscopic Chemistry of MOE, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, China
| | - Yong Zhang
- Key Laboratory of Mesoscopic Chemistry of MOE, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, China
| | - Jiabao Zhao
- Department of Control Science and Intelligent Engineering, School of Management and Engineering, Nanjing University, Nanjing 210093, China
| | - Xu Cheng
- Key Laboratory of Mesoscopic Chemistry of MOE, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, China
- Jiangsu Key Laboratory of Advanced Organic Materials, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, China
| | - Chunlin Chen
- Department of Control Science and Intelligent Engineering, School of Management and Engineering, Nanjing University, Nanjing 210093, China
- Correspondence: (C.C.); (J.M.)
| | - Jing Ma
- Key Laboratory of Mesoscopic Chemistry of MOE, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, China
- Jiangsu Key Laboratory of Advanced Organic Materials, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, China
- Correspondence: (C.C.); (J.M.)
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16
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Peng X, Wang X. Next-generation intelligent laboratories for materials design and manufacturing. MRS BULLETIN 2023; 48:179-185. [PMID: 36960275 PMCID: PMC9970134 DOI: 10.1557/s43577-023-00481-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 01/20/2023] [Indexed: 06/18/2023]
Abstract
The contradiction between the importance of materials to modern society and their slow development process has led to the development of multiple methods to accelerate materials discovery. The recently emerged concept of intelligent laboratories integrates the developments in fields of high-throughput experimentation, automation, theoretical computing, and artificial intelligence to form a system that can autonomously carry out designed experiments and make scientific discoveries. We present the basic concepts and the foundations of this new research paradigm, demonstrate its typical application scenarios through case studies, and envision a collaborative human-machine meta laboratory in the future.
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Affiliation(s)
- Xiting Peng
- Department of Chemical Engineering, Tsinghua University, Beijing, China
| | - Xiaonan Wang
- Department of Chemical Engineering, Tsinghua University, Beijing, China
- Key Laboratory of Industrial Biocatalysis (Tsinghua University), Ministry of Education, Beijing, China
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17
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Bridging the complexity gap in computational heterogeneous catalysis with machine learning. Nat Catal 2023. [DOI: 10.1038/s41929-023-00911-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/03/2023]
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18
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Jing W, Shen H, Qin R, Wu Q, Liu K, Zheng N. Surface and Interface Coordination Chemistry Learned from Model Heterogeneous Metal Nanocatalysts: From Atomically Dispersed Catalysts to Atomically Precise Clusters. Chem Rev 2022; 123:5948-6002. [PMID: 36574336 DOI: 10.1021/acs.chemrev.2c00569] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
The surface and interface coordination structures of heterogeneous metal catalysts are crucial to their catalytic performance. However, the complicated surface and interface structures of heterogeneous catalysts make it challenging to identify the molecular-level structure of their active sites and thus precisely control their performance. To address this challenge, atomically dispersed metal catalysts (ADMCs) and ligand-protected atomically precise metal clusters (APMCs) have been emerging as two important classes of model heterogeneous catalysts in recent years, helping to build bridge between homogeneous and heterogeneous catalysis. This review illustrates how the surface and interface coordination chemistry of these two types of model catalysts determines the catalytic performance from multiple dimensions. The section of ADMCs starts with the local coordination structure of metal sites at the metal-support interface, and then focuses on the effects of coordinating atoms, including their basicity and hardness/softness. Studies are also summarized to discuss the cooperativity achieved by dual metal sites and remote effects. In the section of APMCs, the roles of surface ligands and supports in determining the catalytic activity, selectivity, and stability of APMCs are illustrated. Finally, some personal perspectives on the further development of surface coordination and interface chemistry for model heterogeneous metal catalysts are presented.
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Affiliation(s)
- Wentong Jing
- State Key Laboratory for Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials, and National & Local Joint Engineering Research Center for Preparation Technology of Nanomaterials, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
| | - Hui Shen
- State Key Laboratory for Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials, and National & Local Joint Engineering Research Center for Preparation Technology of Nanomaterials, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
| | - Ruixuan Qin
- State Key Laboratory for Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials, and National & Local Joint Engineering Research Center for Preparation Technology of Nanomaterials, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
| | - Qingyuan Wu
- State Key Laboratory for Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials, and National & Local Joint Engineering Research Center for Preparation Technology of Nanomaterials, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
- Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen 361102, China
| | - Kunlong Liu
- State Key Laboratory for Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials, and National & Local Joint Engineering Research Center for Preparation Technology of Nanomaterials, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
| | - Nanfeng Zheng
- State Key Laboratory for Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials, and National & Local Joint Engineering Research Center for Preparation Technology of Nanomaterials, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
- Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen 361102, China
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