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Cross-platform hyperparameter optimization for machine learning interatomic potentials.

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Bibliographic Details
Title: Cross-platform hyperparameter optimization for machine learning interatomic potentials.
Authors: Thomas du Toit, Daniel F., Deringer, Volker L.
Source: Journal of Chemical Physics; 7/14/2023, Vol. 159 Issue 2, p1-11, 11p
Subject Terms: MACHINE learning, PHYSICAL sciences, PYTHON programming language, DATA modeling
Abstract: Machine-learning (ML)-based interatomic potentials are increasingly popular in material modeling, enabling highly accurate simulations with thousands and millions of atoms. However, the performance of machine-learned potentials depends strongly on the choice of hyperparameters—that is, of those parameters that are set before the model encounters data. This problem is particularly acute where hyperparameters have no intuitive physical interpretation and where the corresponding optimization space is large. Here, we describe an openly available Python package that facilitates hyperparameter optimization across different ML potential fitting frameworks. We discuss methodological aspects relating to the optimization itself and to the selection of validation data, and we show example applications. We expect this package to become part of a wider computational framework to speed up the mainstream adaptation of ML potentials in the physical sciences. [ABSTRACT FROM AUTHOR]
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Database: Complementary Index
Description
Abstract:Machine-learning (ML)-based interatomic potentials are increasingly popular in material modeling, enabling highly accurate simulations with thousands and millions of atoms. However, the performance of machine-learned potentials depends strongly on the choice of hyperparameters—that is, of those parameters that are set before the model encounters data. This problem is particularly acute where hyperparameters have no intuitive physical interpretation and where the corresponding optimization space is large. Here, we describe an openly available Python package that facilitates hyperparameter optimization across different ML potential fitting frameworks. We discuss methodological aspects relating to the optimization itself and to the selection of validation data, and we show example applications. We expect this package to become part of a wider computational framework to speed up the mainstream adaptation of ML potentials in the physical sciences. [ABSTRACT FROM AUTHOR]
ISSN:00219606
DOI:10.1063/5.0155618