气候变化领域集成服务门户(CCPortal): Machine learning potentials for complex aqueous systems made simple

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DOI	10.1073/pnas.2110077118
	Machine learning potentials for complex aqueous systems made simple
	Schran C.; Thiemann F.L.; Rowe P.; Müller E.A.; Marsalek O.; Michaelides A.
发表日期	2021
ISSN	0027-8424
卷号	118 期号:38
英文摘要	Simulation techniques based on accurate and efficient representations of potential energy surfaces are urgently needed for the understanding of complex systems such as solid-liquid interfaces. Here we present a machine learning framework that enables the efficient development and validation of models for complex aqueous systems. Instead of trying to deliver a globally optimal machine learning potential, we propose to develop models applicable to specific thermodynamic state points in a simple and user-friendly process. After an initial ab initio simulation, a machine learning potential is constructed with minimum human effort through a data-driven active learning protocol. Such models can afterward be applied in exhaustive simulations to provide reliable answers for the scientific question at hand or to systematically explore the thermal performance of ab initio methods. We showcase this methodology on a diverse set of aqueous systems comprising bulk water with different ions in solution, water on a titanium dioxide surface, and water confined in nanotubes and between molybdenum disulfide sheets. Highlighting the accuracy of our approach with respect to the underlying ab initio reference, the resulting models are evaluated in detail with an automated validation protocol that includes structural and dynamical properties and the precision of the force prediction of the models. Finally, we demonstrate the capabilities of our approach for the description of water on the rutile titanium dioxide (110) surface to analyze the structure and mobility of water on this surface. Such machine learning models provide a straightforward and uncomplicated but accurate extension of simulation time and length scales for complex systems. © 2021 National Academy of Sciences. All rights reserved.
英文关键词	Aqueous phase; Machine learning potentials; Solid-liquid systems
语种	英语
scopus关键词	fluoride; molybdenum; nanosheet; nanotube; sulfate; titanium dioxide; water; ab initio calculation; aqueous solution; Article; artificial neural network; chemical phenomena; chemical structure; controlled study; diffusion; flow kinetics; machine learning; measurement accuracy; measurement precision; photocatalysis; process optimization; radial distribution function; simulation; solid liquid system; thermodynamics; validation study; vibrational density of state; water mobility
来源期刊	Proceedings of the National Academy of Sciences of the United States of America
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/238379
作者单位	Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, CB2 1EW, United Kingdom; Thomas Young Centre, University College London, London, WC1E 6BT, United Kingdom; London Centre for Nanotechnology, University College London, London, WC1E 6BT, United Kingdom; Department of Physics and Astronomy, University College London, London, WC1E 6BT, United Kingdom; Department of Chemical Engineering, Sargent Centre for Process Systems Engineering, Imperial College London, London, SW7 2AZ, United Kingdom; Charles University, Faculty of Mathematics and Physics, Prague 2, 121 16, Czech Republic
推荐引用方式 GB/T 7714	Schran C.,Thiemann F.L.,Rowe P.,et al. Machine learning potentials for complex aqueous systems made simple[J],2021,118(38).
APA	Schran C.,Thiemann F.L.,Rowe P.,Müller E.A.,Marsalek O.,&Michaelides A..(2021).Machine learning potentials for complex aqueous systems made simple.Proceedings of the National Academy of Sciences of the United States of America,118(38).
MLA	Schran C.,et al."Machine learning potentials for complex aqueous systems made simple".Proceedings of the National Academy of Sciences of the United States of America 118.38(2021).