Comparing machine learning potentials for water

Autor(en)

Pablo Montero de Hijes, Christoph Dellago, Ryosuke Jinnouchi, Bernhard Schmiedmayer, Georg Kresse

Abstrakt

In this paper, we investigate the performance of different machine learning potentials (MLPs) in predicting key thermodynamic properties of water using RPBE + D3. Specifically, we scrutinize kernel-based regression and high-dimensional neural networks trained on a highly accurate dataset consisting of about 1500 structures, as well as a smaller dataset, about half the size, obtained using only on-the-fly learning. This study reveals that despite minor differences between the MLPs, their agreement on observables such as the diffusion constant and pair-correlation functions is excellent, especially for the large training dataset. Variations in the predicted density isobars, albeit somewhat larger, are also acceptable, particularly given the errors inherent to approximate density functional theory. Overall, this study emphasizes the relevance of the database over the fitting method. Finally, this study underscores the limitations of root mean square errors and the need for comprehensive testing, advocating the use of multiple MLPs for enhanced certainty, particularly when simulating complex thermodynamic properties that may not be fully captured by simpler tests.

Organisation(en)

Department für Lithosphärenforschung, Computergestützte Physik und Physik der Weichen Materie, Computergestützte Materialphysik

Externe Organisation(en)

Toyota Central R&D Labs., Inc., VASP Software GmbH

Journal

Journal of Chemical Physics

Band

160

Anzahl der Seiten

12

ISSN

0021-9606

DOI

https://doi.org/10.1063/5.0197105

Publikationsdatum

03-2024

Peer-reviewed

Ja

ÖFOS 2012

103043 Computational Physics, 103029 Statistische Physik, 102019 Machine Learning

ASJC Scopus Sachgebiete

Allgemeine Physik und Astronomie, Physical and Theoretical Chemistry

Link zum Portal

https://ucrisportal.univie.ac.at/de/publications/dcf79e5f-8275-4f4f-bf91-e2e064cdfbd1