The introduction of modern Machine Learning Potentials (MLPs) has led to a paradigm change in the development of potential energy surfaces for atomistic simulations. By providing efficient access to energies and forces, they allow us to perform large-scale simulations of extended systems, which are not directly accessible by demanding first-principles methods. In these simulations, MLPs can reach the accuracy of electronic structure calculations, provided that they have been properly trained and validated using a suitable set of reference data. Due to their highly flexible functional form, the construction of MLPs has to be done with great care. In this Tutorial, we describe the necessary key steps for training reliable MLPs, from data generation via training to final validation. The procedure, which is illustrated for the example of a high-dimensional neural network potential, is general and applicable to many types of MLPs.
Skip Nav Destination
How to train a neural network potential
Article navigation
28 September 2023
Tutorial|
September 27 2023
How to train a neural network potential
Special Collection:
JCP Editors’ Choice 2023
Alea Miako Tokita
;
Alea Miako Tokita
a)
(Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Visualization, Writing – original draft, Writing – review & editing)
Lehrstuhl für Theoretische Chemie II, Ruhr-Universität Bochum
, 44780 Bochum, Germany
and Research Center Chemical Sciences and Sustainability, Research Alliance Ruhr
, 44780 Bochum, Germany
a)Author to whom correspondence should be addressed: [email protected]
Search for other works by this author on:
Jörg Behler
Jörg Behler
b)
(Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Visualization, Writing – original draft, Writing – review & editing)
Lehrstuhl für Theoretische Chemie II, Ruhr-Universität Bochum
, 44780 Bochum, Germany
and Research Center Chemical Sciences and Sustainability, Research Alliance Ruhr
, 44780 Bochum, Germany
Search for other works by this author on:
a)Author to whom correspondence should be addressed: [email protected]
J. Chem. Phys. 159, 121501 (2023)
Article history
Received:
May 31 2023
Accepted:
July 24 2023
Citation
Alea Miako Tokita, Jörg Behler; How to train a neural network potential. J. Chem. Phys. 28 September 2023; 159 (12): 121501. https://doi.org/10.1063/5.0160326
Download citation file:
Pay-Per-View Access
$40.00
Sign In
You could not be signed in. Please check your credentials and make sure you have an active account and try again.
Citing articles via
DeePMD-kit v2: A software package for deep potential models
Jinzhe Zeng, Duo Zhang, et al.
CREST—A program for the exploration of low-energy molecular chemical space
Philipp Pracht, Stefan Grimme, et al.
Rubber wear: Experiment and theory
B. N. J. Persson, R. Xu, et al.