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Wang G, Wang C, Zhang X, Li Z, Zhou J, Sun Z. Machine learning interatomic potential: Bridge the gap between small-scale models and realistic device-scale simulations. iScience 2024; 27:109673. [PMID: 38646181 PMCID: PMC11033164 DOI: 10.1016/j.isci.2024.109673] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/23/2024] Open
Abstract
Machine learning interatomic potential (MLIP) overcomes the challenges of high computational costs in density-functional theory and the relatively low accuracy in classical large-scale molecular dynamics, facilitating more efficient and precise simulations in materials research and design. In this review, the current state of the four essential stages of MLIP is discussed, including data generation methods, material structure descriptors, six unique machine learning algorithms, and available software. Furthermore, the applications of MLIP in various fields are investigated, notably in phase-change memory materials, structure searching, material properties predicting, and the pre-trained universal models. Eventually, the future perspectives, consisting of standard datasets, transferability, generalization, and trade-off between accuracy and complexity in MLIPs, are reported.
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Affiliation(s)
- Guanjie Wang
- School of Materials Science and Engineering, Beihang University, Beijing 100191, China
- School of Integrated Circuit Science and Engineering, Beihang University, Beijing 100191, China
| | - Changrui Wang
- School of Materials Science and Engineering, Beihang University, Beijing 100191, China
| | - Xuanguang Zhang
- School of Materials Science and Engineering, Beihang University, Beijing 100191, China
| | - Zefeng Li
- School of Materials Science and Engineering, Beihang University, Beijing 100191, China
| | - Jian Zhou
- School of Materials Science and Engineering, Beihang University, Beijing 100191, China
| | - Zhimei Sun
- School of Materials Science and Engineering, Beihang University, Beijing 100191, China
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Xie J, Zhou Y, Faizan M, Li Z, Li T, Fu Y, Wang X, Zhang L. Designing semiconductor materials and devices in the post-Moore era by tackling computational challenges with data-driven strategies. NATURE COMPUTATIONAL SCIENCE 2024; 4:322-333. [PMID: 38783137 DOI: 10.1038/s43588-024-00632-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 04/18/2024] [Indexed: 05/25/2024]
Abstract
In the post-Moore's law era, the progress of electronics relies on discovering superior semiconductor materials and optimizing device fabrication. Computational methods, augmented by emerging data-driven strategies, offer a promising alternative to the traditional trial-and-error approach. In this Perspective, we highlight data-driven computational frameworks for enhancing semiconductor discovery and device development by elaborating on their advances in exploring the materials design space, predicting semiconductor properties and optimizing device fabrication, with a concluding discussion on the challenges and opportunities in these areas.
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Affiliation(s)
- Jiahao Xie
- State Key Laboratory of Integrated Optoelectronics, Key Laboratory of Automobile Materials of MOE, Key Laboratory of Material Simulation Methods & Software of MOE, and School of Materials Science and Engineering, Jilin University, Changchun, China
| | - Yansong Zhou
- State Key Laboratory of Superhard Materials, International Center of Computational Method and Software, School of Physics, Jilin University, Changchun, China
| | - Muhammad Faizan
- State Key Laboratory of Integrated Optoelectronics, Key Laboratory of Automobile Materials of MOE, Key Laboratory of Material Simulation Methods & Software of MOE, and School of Materials Science and Engineering, Jilin University, Changchun, China
| | - Zewei Li
- State Key Laboratory of Integrated Optoelectronics, Key Laboratory of Automobile Materials of MOE, Key Laboratory of Material Simulation Methods & Software of MOE, and School of Materials Science and Engineering, Jilin University, Changchun, China
| | - Tianshu Li
- State Key Laboratory of Integrated Optoelectronics, Key Laboratory of Automobile Materials of MOE, Key Laboratory of Material Simulation Methods & Software of MOE, and School of Materials Science and Engineering, Jilin University, Changchun, China
| | - Yuhao Fu
- State Key Laboratory of Superhard Materials, International Center of Computational Method and Software, School of Physics, Jilin University, Changchun, China
| | - Xinjiang Wang
- State Key Laboratory of Integrated Optoelectronics, Key Laboratory of Automobile Materials of MOE, Key Laboratory of Material Simulation Methods & Software of MOE, and School of Materials Science and Engineering, Jilin University, Changchun, China.
| | - Lijun Zhang
- State Key Laboratory of Integrated Optoelectronics, Key Laboratory of Automobile Materials of MOE, Key Laboratory of Material Simulation Methods & Software of MOE, and School of Materials Science and Engineering, Jilin University, Changchun, China.
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Wang FC, Ye QJ, Zhu YC, Li XZ. Crystal-Structure Matches in Solid-Solid Phase Transitions. PHYSICAL REVIEW LETTERS 2024; 132:086101. [PMID: 38457702 DOI: 10.1103/physrevlett.132.086101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 12/01/2023] [Accepted: 01/09/2024] [Indexed: 03/10/2024]
Abstract
The exploration of solid-solid phase transition suffers from the uncertainty of how atoms in two crystal structures match. We devised a theoretical framework to describe and classify crystal-structure matches (CSM). Such description fully exploits the translational and rotational symmetries and is independent of the choice of supercells. This is enabled by the use of the Hermite normal form, an analog of reduced echelon form for integer matrices. With its help, exhausting all CSMs is made possible, which goes beyond the conventional optimization schemes. In an example study of the martensitic transformation of steel, our enumeration algorithm finds many candidate CSMs with lower strains than known mechanisms. Two long-sought CSMs accounting for the most commonly observed Kurdjumov-Sachs orientation relationship and the Nishiyama-Wassermann orientation relationship are unveiled. Given the comprehensiveness and efficiency, our enumeration scheme provide a promising strategy for solid-solid phase transition mechanism research.
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Affiliation(s)
- Fang-Cheng Wang
- State Key Laboratory for Artificial Microstructure and Mesoscopic Physics, Frontier Science Center for Nano-optoelectronics, School of Physics, Peking University, Beijing 100871, People's Republic of China
| | - Qi-Jun Ye
- State Key Laboratory for Artificial Microstructure and Mesoscopic Physics, Frontier Science Center for Nano-optoelectronics, School of Physics, Peking University, Beijing 100871, People's Republic of China
- Interdisciplinary Institute of Light-Element Quantum Materials, Research Center for Light-Element Advanced Materials, and Collaborative Innovation Center of Quantum Matter, Peking University, Beijing 100871, People's Republic of China
| | - Yu-Cheng Zhu
- State Key Laboratory for Artificial Microstructure and Mesoscopic Physics, Frontier Science Center for Nano-optoelectronics, School of Physics, Peking University, Beijing 100871, People's Republic of China
| | - Xin-Zheng Li
- State Key Laboratory for Artificial Microstructure and Mesoscopic Physics, Frontier Science Center for Nano-optoelectronics, School of Physics, Peking University, Beijing 100871, People's Republic of China
- Interdisciplinary Institute of Light-Element Quantum Materials, Research Center for Light-Element Advanced Materials, and Collaborative Innovation Center of Quantum Matter, Peking University, Beijing 100871, People's Republic of China
- Peking University Yangtze Delta Institute of Optoelectronics, Nantong, Jiangsu 226010, People's Republic of China
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Hao Y, Kang Y, Wang S, Chen Z, Lei C, Cao X, Chen L, Li Y, Liu Z, Gong M. Electrode/Electrolyte Synergy for Concerted Promotion of Electron and Proton Transfers toward Efficient Neutral Water Oxidation. Angew Chem Int Ed Engl 2023; 62:e202303200. [PMID: 37278979 DOI: 10.1002/anie.202303200] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Revised: 06/03/2023] [Accepted: 06/06/2023] [Indexed: 06/07/2023]
Abstract
Neutral water oxidation is a crucial half-reaction for various electrochemical applications requiring pH-benign conditions. However, its sluggish kinetics with limited proton and electron transfer rates greatly impacts the overall energy efficiency. In this work, we created an electrode/electrolyte synergy strategy for simultaneously enhancing the proton and electron transfers at the interface toward highly efficient neutral water oxidation. The charge transfer was accelerated between the iridium oxide and in situ formed nickel oxyhydroxide on the electrode end. The proton transfer was expedited by the compact borate environment that originated from hierarchical fluoride/borate anions on the electrolyte end. These concerted promotions facilitated the proton-coupled electron transfer (PCET) events. Due to the electrode/electrolyte synergy, Ir-O and Ir-OO- intermediates could be directly detected by in situ Raman spectroscopy, and the rate-limiting step of Ir-O oxidation was determined. This synergy strategy can extend the scope of optimizing electrocatalytic activities toward more electrode/electrolyte combinations.
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Affiliation(s)
- Yaming Hao
- Department of Chemistry and, Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Fudan University, 200438, Shanghai, P. R. China
| | - Yikun Kang
- Department of Chemistry and, Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Fudan University, 200438, Shanghai, P. R. China
| | - Shaoyan Wang
- Department of Chemistry and, Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Fudan University, 200438, Shanghai, P. R. China
| | - Zhe Chen
- Department of Chemistry and, Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Fudan University, 200438, Shanghai, P. R. China
| | - Can Lei
- Department of Chemistry and, Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Fudan University, 200438, Shanghai, P. R. China
| | - Xueting Cao
- Department of Chemistry and, Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Fudan University, 200438, Shanghai, P. R. China
| | - Lin Chen
- Department of Chemistry and, Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Fudan University, 200438, Shanghai, P. R. China
| | - Yefei Li
- Department of Chemistry and, Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Fudan University, 200438, Shanghai, P. R. China
| | - Zhipan Liu
- Department of Chemistry and, Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Fudan University, 200438, Shanghai, P. R. China
| | - Ming Gong
- Department of Chemistry and, Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Fudan University, 200438, Shanghai, P. R. China
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Liu QY, Chen D, Shang C, Liu ZP. An optimal Fe-C coordination ensemble for hydrocarbon chain growth: a full Fischer-Tropsch synthesis mechanism from machine learning. Chem Sci 2023; 14:9461-9475. [PMID: 37712046 PMCID: PMC10498498 DOI: 10.1039/d3sc02054a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Accepted: 08/11/2023] [Indexed: 09/16/2023] Open
Abstract
Fischer-Tropsch synthesis (FTS, CO + H2 → long-chain hydrocarbons) because of its great significance in industry has attracted huge attention since its discovery. For Fe-based catalysts, after decades of efforts, even the product distribution remains poorly understood due to the lack of information on the active site and the chain growth mechanism. Herein powered by a newly developed machine-learning-based transition state (ML-TS) exploration method to treat properly reaction-induced surface reconstruction, we are able to resolve where and how long-chain hydrocarbons grow on complex in situ-formed Fe-carbide (FeCx) surfaces from thousands of pathway candidates. Microkinetics simulations based on first-principles kinetics data further determine the rate-determining and the selectivity-controlling steps, and reveal the fine details of the product distribution in obeying and deviating from the Anderson-Schulz-Flory law. By showing that all FeCx phases can grow coherently upon each other, we demonstrate that the FTS active site, namely the A-P5 site present on reconstructed Fe3C(031), Fe5C2(510), Fe5C2(021), and Fe7C3(071) terrace surfaces, is not necessarily connected to any particular FeCx phase, rationalizing long-standing structure-activity puzzles. The optimal Fe-C coordination ensemble of the A-P5 site exhibits both Fe-carbide (Fe4C square) and metal Fe (Fe3 trimer) features.
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Affiliation(s)
- Qian-Yu 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
| | - Dongxiao Chen
- 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
| | - 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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Interface engineering of Ni/NiO heterostructures with abundant catalytic active sites for enhanced methanol oxidation electrocatalysis. J Colloid Interface Sci 2023; 630:570-579. [DOI: 10.1016/j.jcis.2022.10.057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2022] [Revised: 09/29/2022] [Accepted: 10/10/2022] [Indexed: 11/05/2022]
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Bobra M. The Shrinking Transistor. PHYSICS 2022. [DOI: 10.1103/physics.15.s75] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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