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Martire S, Decherchi S, Cavalli A. OBIWAN: An Element-Wise Scalable Feed-Forward Neural Network Potential. J Chem Theory Comput 2024. [PMID: 38978155 DOI: 10.1021/acs.jctc.4c00342] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
Estimating the potential energy of a molecular system at a quantum level of theory is a task of paramount importance in computational chemistry. The often employed density functional theory approach allows one to accomplish this task, yet most often at significant computational costs. This prompted the community to develop so-called machine learning potentials to achieve near-quantum accuracy at molecular mechanics computational cost. In this paper, we introduce OBIWAN, a feed-forward neural network that bears some relevant structural properties that also led to the definition of a new kind of general-purpose neural network layer. Its featurization process scales efficiently with newly added atomic species. This allows one to seamlessly add new atom types without requiring to change the topology of the network. Also, this allows one to train on new data sets leveraging a previously trained OBIWAN, hence converging very quickly. This avoids training from scratch and renders the approach more compliant with a green computing perspective.
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Affiliation(s)
- Stefano Martire
- Department of Pharmacy and Biotechnology, University of Bologna, Via Belmeloro 6, Bologna 40126, Italy
- Computational and Chemical Biology, Fondazione Istituto Italiano di Tecnologia, Via Morego 30, Genoa 16163, Italy
| | - Sergio Decherchi
- Data Science and Computation Facility, Fondazione Istituto Italiano di Tecnologia, Via Morego 30, Genoa 16163, Italy
| | - Andrea Cavalli
- Computational and Chemical Biology, Fondazione Istituto Italiano di Tecnologia, Via Morego 30, Genoa 16163, Italy
- Centre Européen de Calcul Atomique et Moléculaire, Ecole Polytechnique Fédérale de Lausanne, Avenue de Forel 3, Lausanne 1015, Switzerland
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Kety K, Namsrai T, Nawaz H, Rostami S, Seriani N. Amorphous MoS2 from a machine learning inter-atomic potential. J Chem Phys 2024; 160:204709. [PMID: 38804492 DOI: 10.1063/5.0211841] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2024] [Accepted: 05/13/2024] [Indexed: 05/29/2024] Open
Abstract
Amorphous molybdenum disulfide has shown potential as a hydrogen evolution catalyst, but the origin of its high activity is unclear, as is its atomic structure. Here, we have developed a classical inter-atomic potential using the charge equilibration neural network method, and we have employed it to generate atomic models of amorphous MoS2 by melting and quenching processes. The amorphous phase contains an abundance of molybdenum and sulfur atoms in low coordination. Besides the 6-coordinated molybdenum typical of the crystalline phases, a substantial fraction displays coordinations 4 and 5. The amorphous phase is also characterized by the appearance of direct S-S bonds. Density functional theory shows that the amorphous phase is metallic, with a considerable contribution of the 4-coordinated molybdenum to the density of states at the Fermi level. S-S bonds are related to the reduction of sulfur, with the excess electrons spread over several molybdenum atoms. Moreover, S-S bond formation is associated with a distinctive broadening of the 3s states, which could be exploited for experimental characterization of the amorphous phases. The large variety of local environments and the high density of electronic states at the Fermi level may play a positive role in increasing the electrocatalytic activity of this compound.
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Affiliation(s)
- Kossi Kety
- ICTP-East African Institute for Fundamental Research (EAIFR), University of Rwanda, Kigali, Rwanda
| | - Tsogbadrakh Namsrai
- Department of Physics, National University of Mongolia, Ulaanbaatar 14201, Mongolia
| | - Huma Nawaz
- The Abdus Salam ICTP, I-34151 Trieste, Italy
- Texas Center for Superconductivity and Department of Physics, University of Houston, Houston, Texas 77204, USA
| | - Samare Rostami
- The Abdus Salam ICTP, I-34151 Trieste, Italy
- European Theoretical Spectroscopy Facility, Institute of Condensed Matter and Nanosciences, Universite Catholique de Louvain, Chemin des étoiles 8, bte L07.03.01, B-1348 Louvain-la-Neuve, Belgium
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Zhang Y, Lin Q, Jiang B. Atomistic neural network representations for chemical dynamics simulations of molecular, condensed phase, and interfacial systems: Efficiency, representability, and generalization. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2022. [DOI: 10.1002/wcms.1645] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Affiliation(s)
- Yaolong Zhang
- Department of Chemical Physics, School of Chemistry and Materials Science, Key Laboratory of Surface and Interface Chemistry and Energy Catalysis of Anhui Higher Education Institutes University of Science and Technology of China Hefei Anhui China
| | - Qidong Lin
- Department of Chemical Physics, School of Chemistry and Materials Science, Key Laboratory of Surface and Interface Chemistry and Energy Catalysis of Anhui Higher Education Institutes University of Science and Technology of China Hefei Anhui China
| | - Bin Jiang
- Department of Chemical Physics, School of Chemistry and Materials Science, Key Laboratory of Surface and Interface Chemistry and Energy Catalysis of Anhui Higher Education Institutes University of Science and Technology of China Hefei Anhui China
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Musil F, Grisafi A, Bartók AP, Ortner C, Csányi G, Ceriotti M. Physics-Inspired Structural Representations for Molecules and Materials. Chem Rev 2021; 121:9759-9815. [PMID: 34310133 DOI: 10.1021/acs.chemrev.1c00021] [Citation(s) in RCA: 135] [Impact Index Per Article: 45.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
The first step in the construction of a regression model or a data-driven analysis, aiming to predict or elucidate the relationship between the atomic-scale structure of matter and its properties, involves transforming the Cartesian coordinates of the atoms into a suitable representation. The development of atomic-scale representations has played, and continues to play, a central role in the success of machine-learning methods for chemistry and materials science. This review summarizes the current understanding of the nature and characteristics of the most commonly used structural and chemical descriptions of atomistic structures, highlighting the deep underlying connections between different frameworks and the ideas that lead to computationally efficient and universally applicable models. It emphasizes the link between properties, structures, their physical chemistry, and their mathematical description, provides examples of recent applications to a diverse set of chemical and materials science problems, and outlines the open questions and the most promising research directions in the field.
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Affiliation(s)
- Felix Musil
- Laboratory of Computational Science and Modeling, IMX, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland.,National Centre for Computational Design and Discovery of Novel Materials (MARVEL), École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
| | - Andrea Grisafi
- Laboratory of Computational Science and Modeling, IMX, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
| | - Albert P Bartók
- Department of Physics and Warwick Centre for Predictive Modelling, School of Engineering, University of Warwick, Coventry CV4 7AL, United Kingdom
| | - Christoph Ortner
- University of British Columbia, Vancouver, British Columbia V6T 1Z2, Canada
| | - Gábor Csányi
- Engineering Laboratory, University of Cambridge, Trumpington Street, Cambridge CB2 1PZ, United Kingdom
| | - Michele Ceriotti
- Laboratory of Computational Science and Modeling, IMX, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland.,National Centre for Computational Design and Discovery of Novel Materials (MARVEL), École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
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Rahmatizad Khajehpasha E, Goedecker S, Ghasemi SA. New strontium titanate polymorphs under high pressure. J Comput Chem 2021; 42:699-705. [PMID: 33556211 DOI: 10.1002/jcc.26490] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2020] [Revised: 01/16/2021] [Accepted: 01/21/2021] [Indexed: 12/28/2022]
Abstract
We report six new dynamically stable structures of SrTiO3 at various pressures ranging from 0 to 200 GPa. These structures were found by exploring the enthalpy surface with the Minima Hopping structure prediction method. The potential energy surface was generated by a machine learned potential, the charge equilibration via neural network technique (CENT), based on an extensive training data set of highly diverse SrTiO3 periodic and cluster structures. All our CENT structures were validated at the level of density functional theory. For our new structures, we performed phonon calculations and NVT molecular dynamics calculations to investigate their dynamical stability. Finally, X-ray diffraction patterns were simulated to help to identify our predicted structures in experiments.
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Affiliation(s)
| | - Stefan Goedecker
- Department of Physics, University of Basel, Klingelbergstrasse 82, Basel, 4056, Switzerland
| | - S Alireza Ghasemi
- Department of Physics, Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan, 45137-66731, Iran
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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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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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Wang X, Gao J. Atomic partial charge predictions for furanoses by random forest regression with atom type symmetry function. RSC Adv 2020; 10:666-673. [PMID: 35494472 PMCID: PMC9048215 DOI: 10.1039/c9ra09337k] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2019] [Accepted: 12/18/2019] [Indexed: 01/04/2023] Open
Abstract
Furanoses that are components for many important biomolecules have complicated conformational spaces due to the flexible ring and exo-cyclic moieties. Machine learning algorithms, which require descriptors as structural inputs, can be used to efficiently compute conformational adaptive (CA) charges to capture the electrostatic potential variations caused by the conformational changes in the molecular mechanics (MM) calculations. In the present study, we introduced atom type symmetry function (ATSF) developed based on atom centered symmetry function (ACSF) for describing conformations for furanoses, in which atoms were categorized by atom types defined by their properties or connectivity in classic molecular mechanics (MM) force field parameters to generate a suitable coordinate size. Random forest regression (RFR) models with ATSF showed improvements for predicting CA charges and dipole moments for furanoses compared to those with ACSF and atom name symmetry functions where atoms were categorized by their unique atom names. The CA charges predicted by RFR models with ATSF showed more comparable reproductions of the carbohydrate-water and carbohydrate-protein interactions computed with RESP charges individually derived from QM calculations than the ensemble-averaged atomic charge sets commonly employed in molecular mechanics force fields, suggesting that the predicted CA charges were capable of including electrostatic variations in their dynamic charge values. Improvements by ATSF showed that categorizing atoms by atom types introduced chemical structural perceptions to descriptors and produced a suitable coordinate size in ATSF to capture key structural features for furanoses. This categorizing scheme also allows ATSF to be readily adopted by other biomolecules thanks to the broad implementations of MM force fields.
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Affiliation(s)
- Xiaocong Wang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University Wuhan China
| | - Jun Gao
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University Wuhan China
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