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Yang ZX, Xie XT, Kang PL, Wang ZX, Shang C, Liu ZP. Many-Body Function Corrected Neural Network with Atomic Attention (MBNN-att) for Molecular Property Prediction. J Chem Theory Comput 2024. [PMID: 39034686 DOI: 10.1021/acs.jctc.4c00660] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/23/2024]
Abstract
Recent years have seen a surge of machine learning (ML) in chemistry for predicting chemical properties, but a low-cost, general-purpose, and high-performance model, desirable to be accessible on central processing unit (CPU) devices, remains not available. For this purpose, here we introduce an atomic attention mechanism into many-body function corrected neural network (MBNN), namely, MBNN-att ML model, to predict both the extensive and intensive properties of molecules and materials. The MBNN-att uses explicit function descriptors as the inputs for the atom-based feed-forward neural network (NN). The output of the NN is designed to be a vector to implement the multihead self-attention mechanism. This vector is split into two parts: the atomic attention weight part and the many-body-function part. The final property is obtained by summing the products of each atomic attention weight and the corresponding many-body function. We show that MBNN-att performs well on all QM9 properties, i.e., errors on all properties, below chemical accuracy, and, in particular, achieves the top performance for the energy-related extensive properties. By systematically comparing with other explicit-function-type descriptor ML models and the graph representation ML models, we demonstrate that the many-body-function framework and atomic attention mechanism are key ingredients for the high performance and the good transferability of MBNN-att in molecular property prediction.
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Affiliation(s)
- Zheng-Xin Yang
- Collaborative Innovation Center of Chemistry for Energy Material, Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Key Laboratory of Computational Physical Science, Department of Chemistry, Fudan University, Shanghai 200433, China
| | - Xin-Tian Xie
- Collaborative Innovation Center of Chemistry for Energy Material, Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Key Laboratory of Computational Physical Science, Department of Chemistry, Fudan University, Shanghai 200433, China
| | - Pei-Lin Kang
- Collaborative Innovation Center of Chemistry for Energy Material, Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Key Laboratory of Computational Physical Science, Department of Chemistry, Fudan University, Shanghai 200433, China
| | - Zhen-Xiong Wang
- Collaborative Innovation Center of Chemistry for Energy Material, Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Key Laboratory of Computational Physical Science, Department of Chemistry, Fudan University, Shanghai 200433, China
| | - Cheng Shang
- Collaborative Innovation Center of Chemistry for Energy Material, Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Key Laboratory of Computational Physical Science, Department of Chemistry, Fudan University, Shanghai 200433, China
- Shanghai Qi Zhi Institution, Shanghai 200030, China
| | - Zhi-Pan Liu
- Collaborative Innovation Center of Chemistry for Energy Material, Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Key Laboratory of Computational Physical Science, Department of Chemistry, Fudan University, Shanghai 200433, China
- Key Laboratory of Synthetic and Self-Assembly Chemistry for Organic Functional Molecules, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai 200032, China
- Shanghai Qi Zhi Institution, Shanghai 200030, China
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2
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Steiner M, Reiher M. A human-machine interface for automatic exploration of chemical reaction networks. Nat Commun 2024; 15:3680. [PMID: 38693117 PMCID: PMC11063077 DOI: 10.1038/s41467-024-47997-9] [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: 08/31/2023] [Accepted: 04/15/2024] [Indexed: 05/03/2024] Open
Abstract
Autonomous reaction network exploration algorithms offer a systematic approach to explore mechanisms of complex chemical processes. However, the resulting reaction networks are so vast that an exploration of all potentially accessible intermediates is computationally too demanding. This renders brute-force explorations unfeasible, while explorations with completely pre-defined intermediates or hard-wired chemical constraints, such as element-specific coordination numbers, are not flexible enough for complex chemical systems. Here, we introduce a STEERING WHEEL to guide an otherwise unbiased automated exploration. The STEERING WHEEL algorithm is intuitive, generally applicable, and enables one to focus on specific regions of an emerging network. It also allows for guiding automated data generation in the context of mechanism exploration, catalyst design, and other chemical optimization challenges. The algorithm is demonstrated for reaction mechanism elucidation of transition metal catalysts. We highlight how to explore catalytic cycles in a systematic and reproducible way. The exploration objectives are fully adjustable, allowing one to harness the STEERING WHEEL for both structure-specific (accurate) calculations as well as for broad high-throughput screening of possible reaction intermediates.
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Affiliation(s)
- Miguel Steiner
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 2, 8093, Zurich, Switzerland
- ETH Zurich, NCCR Catalysis, Vladimir-Prelog-Weg 2, 8093, Zurich, Switzerland
| | - Markus Reiher
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 2, 8093, Zurich, Switzerland.
- ETH Zurich, NCCR Catalysis, Vladimir-Prelog-Weg 2, 8093, Zurich, Switzerland.
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3
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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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4
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Li F, Cheng X, Lu G, Yin YC, Wu YC, Pan R, Luo JD, Huang F, Feng LZ, Lu LL, Ma T, Zheng L, Jiao S, Cao R, Liu ZP, Zhou H, Tao X, Shang C, Yao HB. Amorphous Chloride Solid Electrolytes with High Li-Ion Conductivity for Stable Cycling of All-Solid-State High-Nickel Cathodes. J Am Chem Soc 2023; 145:27774-27787. [PMID: 38079498 DOI: 10.1021/jacs.3c10602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2023]
Abstract
Solid electrolytes (SEs) are central components that enable high-performance, all-solid-state lithium batteries (ASSLBs). Amorphous SEs hold great potential for ASSLBs because their grain-boundary-free characteristics facilitate intact solid-solid contact and uniform Li-ion conduction for high-performance cathodes. However, amorphous oxide SEs with limited ionic conductivities and glassy sulfide SEs with narrow electrochemical windows cannot sustain high-nickel cathodes. Herein, we report a class of amorphous Li-Ta-Cl-based chloride SEs possessing high Li-ion conductivity (up to 7.16 mS cm-1) and low Young's modulus (approximately 3 GPa) to enable excellent Li-ion conduction and intact physical contact among rigid components in ASSLBs. We reveal that the amorphous Li-Ta-Cl matrix is composed of LiCl43-, LiCl54-, LiCl65- polyhedra, and TaCl6- octahedra via machine-learning simulation, solid-state 7Li nuclear magnetic resonance, and X-ray absorption analysis. Attractively, our amorphous chloride SEs exhibit excellent compatibility with high-nickel cathodes. We demonstrate that ASSLBs comprising amorphous chloride SEs and high-nickel single-crystal cathodes (LiNi0.88Co0.07Mn0.05O2) exhibit ∼99% capacity retention after 800 cycles at ∼3 C under 1 mA h cm-2 and ∼80% capacity retention after 75 cycles at 0.2 C under a high areal capacity of 5 mA h cm-2. Most importantly, a stable operation of up to 9800 cycles with a capacity retention of ∼77% at a high rate of 3.4 C can be achieved in a freezing environment of -10 °C. Our amorphous chloride SEs will pave the way to realize high-performance high-nickel cathodes for high-energy-density ASSLBs.
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Affiliation(s)
- Feng Li
- Division of Nanomaterials and Chemistry, Hefei National Research Center for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei 230026, Anhui, China
| | - Xiaobin Cheng
- Department of Applied Chemistry, University of Science and Technology of China, Hefei 230026, Anhui, China
| | - Gongxun Lu
- College of Materials Science and Engineering, Zhejiang University of Technology, Hangzhou 310014, Zhejiang, China
| | - Yi-Chen Yin
- Department of Applied Chemistry, University of Science and Technology of China, Hefei 230026, Anhui, China
| | - Ye-Chao Wu
- Department of Applied Chemistry, University of Science and Technology of China, Hefei 230026, Anhui, China
- Hefei Gotion High-tech Power Energy Co., Ltd., Hefei 230012, Anhui, China
| | - Ruijun Pan
- Hefei Gotion High-tech Power Energy Co., Ltd., Hefei 230012, Anhui, China
| | - Jin-Da Luo
- Department of Applied Chemistry, University of Science and Technology of China, Hefei 230026, Anhui, China
| | - Fanyang Huang
- Division of Nanomaterials and Chemistry, Hefei National Research Center for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei 230026, Anhui, China
- Department of Materials Science and Engineering, CAS Key Laboratory of Materials for Energy Conversion, University of Science and Technology of China, Hefei 230026, Anhui, China
| | - Li-Zhe Feng
- Department of Applied Chemistry, University of Science and Technology of China, Hefei 230026, Anhui, China
| | - Lei-Lei Lu
- Division of Nanomaterials and Chemistry, Hefei National Research Center for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei 230026, Anhui, China
| | - Tao Ma
- Division of Nanomaterials and Chemistry, Hefei National Research Center for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei 230026, Anhui, China
| | - Lirong Zheng
- Institute of High Energy Physics, the Chinese Academy of Sciences, Beijing 100049, China
| | - Shuhong Jiao
- Division of Nanomaterials and Chemistry, Hefei National Research Center for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei 230026, Anhui, China
- Department of Materials Science and Engineering, CAS Key Laboratory of Materials for Energy Conversion, University of Science and Technology of China, Hefei 230026, Anhui, China
| | - Ruiguo Cao
- Division of Nanomaterials and Chemistry, Hefei National Research Center for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei 230026, Anhui, China
- Department of Materials Science and Engineering, CAS Key Laboratory of Materials for Energy Conversion, University of Science and Technology of China, Hefei 230026, Anhui, China
| | - Zhi-Pan Liu
- Collaborative Innovation Center of Chemistry for Energy Material, Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Key Laboratory of Computational Physical Science, Department of Chemistry, Fudan University, Shanghai 200433, China
- Shanghai Qi Zhi Institute, Shanghai 200030, China
| | - Hongmin Zhou
- Division of Nanomaterials and Chemistry, Hefei National Research Center for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei 230026, Anhui, China
| | - Xinyong Tao
- College of Materials Science and Engineering, Zhejiang University of Technology, Hangzhou 310014, Zhejiang, China
| | - Cheng Shang
- Collaborative Innovation Center of Chemistry for Energy Material, Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Key Laboratory of Computational Physical Science, Department of Chemistry, Fudan University, Shanghai 200433, China
- Shanghai Qi Zhi Institute, Shanghai 200030, China
| | - Hong-Bin Yao
- Division of Nanomaterials and Chemistry, Hefei National Research Center for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei 230026, Anhui, China
- Department of Applied Chemistry, University of Science and Technology of China, Hefei 230026, Anhui, China
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5
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Kang PL, Yang ZX, Shang C, Liu ZP. Global Neural Network Potential with Explicit Many-Body Functions for Improved Descriptions of Complex Potential Energy Surface. J Chem Theory Comput 2023; 19:7972-7981. [PMID: 37856312 DOI: 10.1021/acs.jctc.3c00873] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2023]
Abstract
The high dimensional machine learning potential (MLP) that has developed rapidly in the past decade represents a giant step forward in large-scale atomic simulation for complex systems. The long-range interaction and the poor description of chemical reactions are typical problems of high dimensional MLP, which are mainly caused by the poor structure discrimination of the atom-centered ML model. Herein, we propose a low-cost neural-network-based MLP architecture for fitting global potential energy surface data, namely, G-MBNN, that can offer improved energy and force resolution on a complex potential energy surface. In G-MBNN, a set of many-body energy terms based on the local atomic environment are explicitly included in computing the total energy─the total energy of the system is written as the sum of atomic energy and many-body energy contributions. These extra many-body energy terms are computationally low-cost and, importantly, can provide easy access to delicate energy terms in complex systems such as very short repulsion, long-range attractions, and sensitive angular-dependent covalent interactions. We implement G-MBNN in the LASP code and demonstrate the improved accuracy of the new framework in representative systems, including ternary-element energy materials LiCoOx, TiO2 with defects, and a series of organic reactions.
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Affiliation(s)
- Pei-Lin Kang
- Collaborative Innovation Center of Chemistry for Energy Material (iChem), Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Key Laboratory of Computational Physical Science, Department of Chemistry, Fudan University, Shanghai 200433, China
| | - Zheng-Xin Yang
- Collaborative Innovation Center of Chemistry for Energy Material (iChem), Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Key Laboratory of Computational Physical Science, Department of Chemistry, Fudan University, Shanghai 200433, China
| | - Cheng Shang
- Collaborative Innovation Center of Chemistry for Energy Material (iChem), Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Key Laboratory of Computational Physical Science, Department of Chemistry, Fudan University, Shanghai 200433, China
| | - Zhi-Pan Liu
- Collaborative Innovation Center of Chemistry for Energy Material (iChem), Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Key Laboratory of Computational Physical Science, Department of Chemistry, Fudan University, Shanghai 200433, China
- Key Laboratory of Synthetic and Self-Assembly Chemistry for Organic Functional Molecules, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai 200032, China
- Shanghai Qi Zhi Institution, Shanghai 200030, China
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6
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Long C, Liu X, Wan K, Jiang Y, An P, Yang C, Wu G, Wang W, Guo J, Li L, Pang K, Li Q, Cui C, Liu S, Tan T, Tang Z. Regulating reconstruction of oxide-derived Cu for electrochemical CO 2 reduction toward n-propanol. SCIENCE ADVANCES 2023; 9:eadi6119. [PMID: 37889974 PMCID: PMC10610896 DOI: 10.1126/sciadv.adi6119] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Accepted: 09/25/2023] [Indexed: 10/29/2023]
Abstract
Oxide-derived copper (OD-Cu) is the most efficient and likely practical electrocatalyst for CO2 reduction toward multicarbon products. However, the inevitable but poorly understood reconstruction from the pristine state to the working state of OD-Cu under strong reduction conditions largely hinders the rational construction of catalysts toward multicarbon products, especially C3 products like n-propanol. Here, we simulate the reconstruction of CuO and Cu2O into their derived Cu by molecular dynamics, revealing that CuO-derived Cu (CuOD-Cu) intrinsically has a richer population of undercoordinated Cu sites and higher surficial Cu atom density than the counterpart Cu2O-derived Cu (Cu2OD-Cu) because of the vigorous oxygen removal. In situ spectroscopes disclose that the coordination number of CuOD-Cu is considerably lower than that of Cu2OD-Cu, enabling the fast kinetics of CO2 reaction and strengthened binding of *C2 intermediate(s). Benefiting from the rich undercoordinated Cu sites, CuOD-Cu achieves remarkable n-propanol faradaic efficiency up to ~17.9%, whereas the Cu2OD-Cu dominantly generates formate.
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Affiliation(s)
- Chang Long
- CAS Key Laboratory of Nanosystem and Hierarchical Fabrication, CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology, Chinese Academy of Sciences, Beijing 100190, P. R. China
- School of Nanoscience and Technology, University of Chinese Academy of Sciences, Beijing 100049, P. R. China
- Molecular Electrochemistry Laboratory, Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China
- MOE Key Laboratory of Micro-systems and Micro-structures Manufacturing, Harbin Institute of Technology, Harbin, 150080, P. R. China
| | - Xiaolong Liu
- School of Nanoscience and Technology, University of Chinese Academy of Sciences, Beijing 100049, P. R. China
- CAS Key Laboratory of Theoretical and Computational Nanoscience, CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology, Chinese Academy of Sciences, Beijing 100190, P. R. China
| | - Kaiwei Wan
- School of Nanoscience and Technology, University of Chinese Academy of Sciences, Beijing 100049, P. R. China
- CAS Key Laboratory of Theoretical and Computational Nanoscience, CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology, Chinese Academy of Sciences, Beijing 100190, P. R. China
| | - Yuheng Jiang
- CAS Key Laboratory of Nanosystem and Hierarchical Fabrication, CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology, Chinese Academy of Sciences, Beijing 100190, P. R. China
| | - Pengfei An
- Beijing Synchrotron Radiation Facility, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, P. R. China
| | - Caoyu Yang
- CAS Key Laboratory of Nanosystem and Hierarchical Fabrication, CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology, Chinese Academy of Sciences, Beijing 100190, P. R. China
| | - Guoling Wu
- CAS Key Laboratory of Nanosystem and Hierarchical Fabrication, CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology, Chinese Academy of Sciences, Beijing 100190, P. R. China
| | - Wenyang Wang
- CAS Key Laboratory of Nanosystem and Hierarchical Fabrication, CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology, Chinese Academy of Sciences, Beijing 100190, P. R. China
- School of Nanoscience and Technology, University of Chinese Academy of Sciences, Beijing 100049, P. R. China
| | - Jun Guo
- State Key Laboratory of Separation Membranes and Membrane Processes, School of Chemistry, Tiangong University, Tianjin 300387, P. R. China
| | - Lei Li
- Molecular Electrochemistry Laboratory, Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China
| | - Kanglei Pang
- Department of Materials and Environmental Chemistry, Stockholm University, Stockholm 10691, Sweden
| | - Qun Li
- CAS Key Laboratory of Nanosystem and Hierarchical Fabrication, CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology, Chinese Academy of Sciences, Beijing 100190, P. R. China
| | - Chunhua Cui
- Molecular Electrochemistry Laboratory, Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China
| | - Shaoqin Liu
- MOE Key Laboratory of Micro-systems and Micro-structures Manufacturing, Harbin Institute of Technology, Harbin, 150080, P. R. China
| | - Ting Tan
- School of Nanoscience and Technology, University of Chinese Academy of Sciences, Beijing 100049, P. R. China
- CAS Key Laboratory of Theoretical and Computational Nanoscience, CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology, Chinese Academy of Sciences, Beijing 100190, P. R. China
| | - Zhiyong Tang
- CAS Key Laboratory of Nanosystem and Hierarchical Fabrication, CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology, Chinese Academy of Sciences, Beijing 100190, P. R. China
- School of Nanoscience and Technology, University of Chinese Academy of Sciences, Beijing 100049, P. R. China
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7
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Chen L, Li XT, Ma S, Hu YF, Shang C, Liu ZP. Highly Selective Low-Temperature Acetylene Semihydrogenation Guided by Multiscale Machine Learning. ACS Catal 2022. [DOI: 10.1021/acscatal.2c04379] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Affiliation(s)
- Lin Chen
- Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Key Laboratory of Computational Physical Science, Department of Chemistry, Fudan University, Shanghai200433, People’s Republic of China
| | - Xiao-Tian Li
- Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Key Laboratory of Computational Physical Science, Department of Chemistry, Fudan University, Shanghai200433, People’s Republic of China
| | - Sicong Ma
- Key Laboratory of Synthetic and Self-Assembly Chemistry for Organic Functional Molecules, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai200032, People’s Republic of China
| | - Yi-Fan Hu
- Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Key Laboratory of Computational Physical Science, Department of Chemistry, Fudan University, Shanghai200433, People’s Republic of China
| | - Cheng Shang
- Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Key Laboratory of Computational Physical Science, Department of Chemistry, Fudan University, Shanghai200433, People’s Republic of China
| | - Zhi-Pan Liu
- Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Key Laboratory of Computational Physical Science, Department of Chemistry, Fudan University, Shanghai200433, People’s Republic of China
- Key Laboratory of Synthetic and Self-Assembly Chemistry for Organic Functional Molecules, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai200032, People’s Republic of China
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8
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Kang PL, Shi YF, Shang C, Liu ZP. Artificial intelligence pathway search to resolve catalytic glycerol hydrogenolysis selectivity. Chem Sci 2022; 13:8148-8160. [PMID: 35919423 PMCID: PMC9278456 DOI: 10.1039/d2sc02107b] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Accepted: 06/20/2022] [Indexed: 11/29/2022] Open
Abstract
The complex interaction between molecules and catalyst surfaces leads to great difficulties in understanding and predicting the activity and selectivity in heterogeneous catalysis. Here we develop an end-to-end artificial intelligence framework for the activity prediction of heterogeneous catalytic systems (AI-Cat method), which takes simple inputs from names of molecules and metal catalysts and outputs the reaction energy profile from the input molecule to low energy pathway products. The AI-Cat method combines two neural network models, one for predicting reaction patterns and the other for providing the reaction barrier and energy, with a Monte Carlo tree search to resolve the low energy pathways in a reaction network. We then apply AI-Cat to resolve the reaction network of glycerol hydrogenolysis on Cu surfaces, which is a typical selective C-O bond activation system and of key significance for biomass-derived polyol utilization. We show that glycerol hydrogenolysis features a huge reaction network of relevant candidates, containing 420 reaction intermediates and 2467 elementary reactions. Among them, the surface-mediated enol-keto tautomeric resonance is a key step to facilitate the primary C-OH bond breaking and thus selects 1,2-propanediol as the major product on Cu catalysts. 1,3-Propanediol can only be produced under strong acidic conditions and high surface H coverage by following a hydrogenation-dehydration pathway. AI-Cat further discovers six low-energy reaction patterns for C-O bond activation on metals that is of general significance to polyol catalysis. Our results demonstrate that the reaction prediction for complex heterogeneous catalysis is now feasible with AI-based atomic simulation and a Monte Carlo tree search.
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Affiliation(s)
- Pei-Lin Kang
- Collaborative Innovation Center of Chemistry for Energy Material, Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Key Laboratory of Computational Physical Science, Department of Chemistry, Fudan University Shanghai 200433 China
| | - Yun-Fei Shi
- Collaborative Innovation Center of Chemistry for Energy Material, Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Key Laboratory of Computational Physical Science, Department of Chemistry, Fudan University Shanghai 200433 China
| | - Cheng Shang
- Collaborative Innovation Center of Chemistry for Energy Material, Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Key Laboratory of Computational Physical Science, Department of Chemistry, Fudan University Shanghai 200433 China
- Shanghai Qi Zhi Institution Shanghai 200030 China
| | - Zhi-Pan Liu
- Collaborative Innovation Center of Chemistry for Energy Material, Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Key Laboratory of Computational Physical Science, Department of Chemistry, Fudan University Shanghai 200433 China
- Shanghai Qi Zhi Institution Shanghai 200030 China
- Key Laboratory of Synthetic and Self-Assembly Chemistry for Organic Functional Molecules, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences Shanghai 200032 China
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9
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Ma S, Liu ZP. Machine learning potential era of zeolite simulation. Chem Sci 2022; 13:5055-5068. [PMID: 35655579 PMCID: PMC9093109 DOI: 10.1039/d2sc01225a] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Accepted: 04/05/2022] [Indexed: 11/21/2022] Open
Abstract
Zeolites, owing to their great variety and complexity in structure and wide applications in chemistry, have long been the hot topic in chemical research. This perspective first presents a short retrospect of theoretical investigations on zeolites using the tools from classical force fields to quantum mechanics calculations and to the latest machine learning (ML) potential simulations. ML potentials as the next-generation technique for atomic simulation open new avenues to simulate and interpret zeolite systems and thus hold great promise for finally predicting the structure-functionality relation of zeolites. Recent advances using ML potentials are then summarized from two main aspects: the origin of zeolite stability and the mechanism of zeolite-related catalytic reactions. We also discussed the possible scenarios of ML potential application aiming to provide instantaneous and easy access of zeolite properties. These advanced applications could now be accomplished by combining cloud-computing-based techniques with ML potential-based atomic simulations. The future development of ML potentials for zeolites in the respects of improving the calculation accuracy, expanding the application scope and constructing the zeolite-related datasets is finally outlooked.
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Affiliation(s)
- Sicong Ma
- Key Laboratory of Synthetic and Self-Assembly Chemistry for Organic Functional Molecules, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences Shanghai 200032 China
| | - Zhi-Pan Liu
- Key Laboratory of Synthetic and Self-Assembly Chemistry for Organic Functional Molecules, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences Shanghai 200032 China
- Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Key Laboratory of Computational Physical Science, Department of Chemistry, Fudan University Shanghai 200433 China
- Shanghai Qi Zhi Institution Shanghai 200030 China
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10
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Xu S, Wu L, Li Z. Nucleation of Water Clusters in Gas Phase: A Computational Study Based on Neural Network Potential and Enhanced Sampling ※. ACTA CHIMICA SINICA 2022. [DOI: 10.6023/a22010003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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