Machine learning potentials for complex aqueous systems made simple
File(s)
Published version
Author(s)
Type
Journal Article
Abstract
Simulation techniques based on accurate and efficient representations of potential energy surfaces are urgently needed for the understanding of complex aqueous 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 afterwards be applied in
exhaustive simulations to provide reliable answers for the scientific
question at hand, or 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, as well as 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.
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 afterwards be applied in
exhaustive simulations to provide reliable answers for the scientific
question at hand, or 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, as well as 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.
Date Issued
2021-09-21
Date Acceptance
2021-07-27
Citation
Proceedings of the National Academy of Sciences of USA, 2021, 38 (118), pp.1-8
ISSN
0027-8424
Publisher
National Academy of Sciences
Start Page
1
End Page
8
Journal / Book Title
Proceedings of the National Academy of Sciences of USA
Volume
38
Issue
118
Copyright Statement
© 2021 the Author(s). Published by PNAS. This open access article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).
Identifier
https://www.pnas.org/content/118/38/e2110077118/
Subjects
aqueous phase
machine learning potentials
solid–liquid systems
Publication Status
Published
Date Publish Online
2021-09-13