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Li H, Jiao Y, Davey K, Qiao SZ. Data-Driven Machine Learning for Understanding Surface Structures of Heterogeneous Catalysts. Angew Chem Int Ed Engl 2023; 62:e202216383. [PMID: 36509704 DOI: 10.1002/anie.202216383] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 12/11/2022] [Accepted: 12/12/2022] [Indexed: 12/15/2022]
Abstract
The design of heterogeneous catalysts is necessarily surface-focused, generally achieved via optimization of adsorption energy and microkinetic modelling. A prerequisite is to ensure the adsorption energy is physically meaningful is the stable existence of the conceived active-site structure on the surface. The development of improved understanding of the catalyst surface, however, is challenging practically because of the complex nature of dynamic surface formation and evolution under in-situ reactions. We propose therefore data-driven machine-learning (ML) approaches as a solution. In this Minireview we summarize recent progress in using machine-learning to search and predict (meta)stable structures, assist operando simulation under reaction conditions and micro-environments, and critically analyze experimental characterization data. We conclude that ML will become the new norm to lower costs associated with discovery and design of optimal heterogeneous catalysts.
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Affiliation(s)
- Haobo Li
- School of Chemical Engineering and Advanced Materials, The University of Adelaide, Adelaide, SA 5005, Australia
| | - Yan Jiao
- School of Chemical Engineering and Advanced Materials, The University of Adelaide, Adelaide, SA 5005, Australia
| | - Kenneth Davey
- School of Chemical Engineering and Advanced Materials, The University of Adelaide, Adelaide, SA 5005, Australia
| | - Shi-Zhang Qiao
- School of Chemical Engineering and Advanced Materials, The University of Adelaide, Adelaide, SA 5005, Australia
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2
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Affiliation(s)
- Jörg Behler
- Universität Göttingen, Institut für Physikalische Chemie, Theoretische Chemie, Tammannstraße 6, 37077 Göttingen, Germany
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Ko TW, Finkler JA, Goedecker S, Behler J. A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer. Nat Commun 2021; 12:398. [PMID: 33452239 PMCID: PMC7811002 DOI: 10.1038/s41467-020-20427-2] [Citation(s) in RCA: 156] [Impact Index Per Article: 52.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2020] [Accepted: 11/18/2020] [Indexed: 11/16/2022] Open
Abstract
Machine learning potentials have become an important tool for atomistic simulations in many fields, from chemistry via molecular biology to materials science. Most of the established methods, however, rely on local properties and are thus unable to take global changes in the electronic structure into account, which result from long-range charge transfer or different charge states. In this work we overcome this limitation by introducing a fourth-generation high-dimensional neural network potential that combines a charge equilibration scheme employing environment-dependent atomic electronegativities with accurate atomic energies. The method, which is able to correctly describe global charge distributions in arbitrary systems, yields much improved energies and substantially extends the applicability of modern machine learning potentials. This is demonstrated for a series of systems representing typical scenarios in chemistry and materials science that are incorrectly described by current methods, while the fourth-generation neural network potential is in excellent agreement with electronic structure calculations. Machine learning potentials do not account for long-range charge transfer. Here the authors introduce a fourth-generation high-dimensional neural network potential including non-local information of charge populations that is able to provide forces, charges and energies in excellent agreement with DFT data.
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Affiliation(s)
- Tsz Wai Ko
- Universität Göttingen, Institut für Physikalische Chemie, Theoretische Chemie, Tammannstraße 6, 37077, Göttingen, Germany.
| | - Jonas A Finkler
- Department of Physics, Universität Basel, Klingelbergstrasse 82, 4056, Basel, Switzerland.
| | - Stefan Goedecker
- Department of Physics, Universität Basel, Klingelbergstrasse 82, 4056, Basel, Switzerland
| | - Jörg Behler
- Universität Göttingen, Institut für Physikalische Chemie, Theoretische Chemie, Tammannstraße 6, 37077, Göttingen, Germany
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Tong Q, Gao P, Liu H, Xie Y, Lv J, Wang Y, Zhao J. Combining Machine Learning Potential and Structure Prediction for Accelerated Materials Design and Discovery. J Phys Chem Lett 2020; 11:8710-8720. [PMID: 32955889 DOI: 10.1021/acs.jpclett.0c02357] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
The theoretical structure prediction method via quantum mechanical atomistic simulations such as density functional theory (DFT), based solely on chemical composition, has already become a routine tool to determine the structures of physical and chemical systems, e.g., solids and clusters. However, the application of DFT to more realistic simulations, to a large extent, is impeded because of the unfavorable scaling of the computational cost with respect to the system size. During recent years, the machine learning potential (MLP) method has been rapidly rising as an accurate and efficient tool for atomistic simulations. In this Perspective, we provide an introduction to the basic principles and advantages of the combination of structure prediction and MLP, as well as the challenges and opportunities associated with this promising approach.
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Affiliation(s)
- Qunchao Tong
- International Center for Computational Method & Software, College of Physics, Jilin University, Changchun 130012, China
- State Key Laboratory of Superhard Materials, College of Physics, Jilin University, Changchun 130012, China
| | - Pengyue Gao
- International Center for Computational Method & Software, College of Physics, Jilin University, Changchun 130012, China
| | - Hanyu Liu
- International Center for Computational Method & Software, College of Physics, Jilin University, Changchun 130012, China
- Key Laboratory of Physics and Technology for Advanced Batteries (Ministry of Education), Jilin University, Changchun 130012, China
- International Center of Future Science, Jilin University, Changchun 130012, China
| | - Yu Xie
- International Center for Computational Method & Software, College of Physics, Jilin University, Changchun 130012, China
- Key Laboratory of Physics and Technology for Advanced Batteries (Ministry of Education), Jilin University, Changchun 130012, China
| | - Jian Lv
- International Center for Computational Method & Software, College of Physics, Jilin University, Changchun 130012, China
- State Key Laboratory of Superhard Materials, College of Physics, Jilin University, Changchun 130012, China
| | - Yanchao Wang
- International Center for Computational Method & Software, College of Physics, Jilin University, Changchun 130012, China
- State Key Laboratory of Superhard Materials, College of Physics, Jilin University, Changchun 130012, China
| | - Jijun Zhao
- Key Laboratory of Materials Modification by Laser, Ion and Electron Beams, (Dalian University of Technology), Ministry of Education, Dalian 116024, China
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Deringer VL, Pickard CJ, Proserpio DM. Hierarchically Structured Allotropes of Phosphorus from Data-Driven Exploration. Angew Chem Int Ed Engl 2020; 59:15880-15885. [PMID: 32497368 PMCID: PMC7540597 DOI: 10.1002/anie.202005031] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2020] [Revised: 05/25/2020] [Indexed: 11/23/2022]
Abstract
The discovery of materials is increasingly guided by quantum-mechanical crystal-structure prediction, but the structural complexity in bulk and nanoscale materials remains a bottleneck. Here we demonstrate how data-driven approaches can vastly accelerate the search for complex structures, combining a machine-learning (ML) model for the potential-energy surface with efficient, fragment-based searching. We use the characteristic building units observed in Hittorf's and fibrous phosphorus to seed stochastic ("random") structure searches over hundreds of thousands of runs. Our study identifies a family of hierarchically structured allotropes based on a P8 cage as principal building unit, including one-dimensional (1D) single and double helix structures, nanowires, and two-dimensional (2D) phosphorene allotropes with square-lattice and kagome topologies. These findings yield new insight into the intriguingly diverse structural chemistry of phosphorus, and they provide an example for how ML methods may, in the long run, be expected to accelerate the discovery of hierarchical nanostructures.
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Affiliation(s)
- Volker L. Deringer
- Department of ChemistryInorganic Chemistry LaboratoryUniversity of OxfordOxfordOX1 3QRUK
| | - Chris J. Pickard
- Department of Materials Science and MetallurgyUniversity of CambridgeCambridgeCB3 0FSUK
- Advanced Institute for Materials ResearchTohoku University2-1-1 Katahira, AobaSendai980-8577Japan
| | - Davide M. Proserpio
- Dipartimento di ChimicaUniversità degli Studi di MilanoMilanoItaly
- Samara Center for Theoretical Materials Science (SCTMS)Samara State Technical University443100SamaraRussia
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Deringer VL, Pickard CJ, Proserpio DM. Hierarchically Structured Allotropes of Phosphorus from Data‐Driven Exploration. Angew Chem Int Ed Engl 2020. [DOI: 10.1002/ange.202005031] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Volker L. Deringer
- Department of Chemistry Inorganic Chemistry Laboratory University of Oxford Oxford OX1 3QR UK
| | - Chris J. Pickard
- Department of Materials Science and Metallurgy University of Cambridge Cambridge CB3 0FS UK
- Advanced Institute for Materials Research Tohoku University 2-1-1 Katahira, Aoba Sendai 980-8577 Japan
| | - Davide M. Proserpio
- Dipartimento di Chimica Università degli Studi di Milano Milano Italy
- Samara Center for Theoretical Materials Science (SCTMS) Samara State Technical University 443100 Samara Russia
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Shimamura K, Fukushima S, Koura A, Shimojo F, Misawa M, Kalia RK, Nakano A, Vashishta P, Matsubara T, Tanaka S. Guidelines for creating artificial neural network empirical interatomic potential from first-principles molecular dynamics data under specific conditions and its application to α-Ag 2Se. J Chem Phys 2019; 151:124303. [PMID: 31575208 DOI: 10.1063/1.5116420] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
First-principles molecular dynamics (FPMD) simulations are highly accurate, but due to their high calculation cost, the computational scale is often limited to hundreds of atoms and few picoseconds under specific temperature and pressure conditions. We present here the guidelines for creating artificial neural network empirical interatomic potential (ANN potential) trained with such a limited FPMD data, which can perform long time scale MD simulations at least under the same conditions. The FPMD data for training are prepared on the basis of the convergence of radial distribution function [g(r)]. While training the ANN using total energy and atomic forces of the FPMD data, the error of pressure is also monitored and minimized. To create further robust potential, we add a small amount of FPMD data to reproduce the interaction between two atoms that are close to each other. ANN potentials for α-Ag2Se were created as an application example, and it has been confirmed that not only g(r) and mean square displacements but also the specific heat requiring a long time scale simulation matched the FPMD and the experimental values. In addition, the MD simulation using the ANN potential achieved over 104 acceleration over the FPMD one. The guidelines proposed here mitigate the creation difficulty of the ANN potential, and a lot of FPMD data sleeping on the hard disk after the research may be put on the front stage again.
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Affiliation(s)
- Kohei Shimamura
- Graduate School of System Informatics, Kobe University, Kobe 657-8501, Japan
| | - Shogo Fukushima
- Department of Physics, Kumamoto University, Kumamoto 860-8555, Japan
| | - Akihide Koura
- Department of Physics, Kumamoto University, Kumamoto 860-8555, Japan
| | - Fuyuki Shimojo
- Department of Physics, Kumamoto University, Kumamoto 860-8555, Japan
| | - Masaaki Misawa
- Faculty of Science and Engineering, Kyushu Sangyo University, Fukuoka 813-8503, Japan
| | - Rajiv K Kalia
- Collaboratory for Advanced Computing and Simulations, University of Southern California, Los Angeles, California 90089-0242, USA
| | - Aiichiro Nakano
- Collaboratory for Advanced Computing and Simulations, University of Southern California, Los Angeles, California 90089-0242, USA
| | - Priya Vashishta
- Collaboratory for Advanced Computing and Simulations, University of Southern California, Los Angeles, California 90089-0242, USA
| | - Takashi Matsubara
- Graduate School of System Informatics, Kobe University, Kobe 657-8501, Japan
| | - Shigenori Tanaka
- Graduate School of System Informatics, Kobe University, Kobe 657-8501, Japan
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