Machine learning potentials for complex aqueous made
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F21%3A10439541" target="_blank" >RIV/00216208:11320/21:10439541 - isvavai.cz</a>
Result on the web
<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=Dueh7__-8y" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=Dueh7__-8y</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1073/pnas.2110077118" target="_blank" >10.1073/pnas.2110077118</a>
Alternative languages
Result language
angličtina
Original language name
Machine learning potentials for complex aqueous made
Original language description
Simulation techniques based on accurate and efficient repre-sentations 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 glob-ally 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 simu-lation, 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 con -fined 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 evalu-ated 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 capa-bilities 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 accu-rate extension of simulation time and length scales for complex systems.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10301 - Atomic, molecular and chemical physics (physics of atoms and molecules including collision, interaction with radiation, magnetic resonances, Mössbauer effect)
Result continuities
Project
<a href="/en/project/EF18_070%2F0010462" target="_blank" >EF18_070/0010462: International mobility of researchers at Charles University (MSCA-IF II)</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Proceedings of the National Academy of Sciences of the United States of America
ISSN
0027-8424
e-ISSN
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Volume of the periodical
118
Issue of the periodical within the volume
38
Country of publishing house
US - UNITED STATES
Number of pages
8
Pages from-to
e2110077118
UT code for WoS article
000702151600016
EID of the result in the Scopus database
2-s2.0-85114880223