How to train a neural network potential

Author:

Tokita Alea Miako12ORCID,Behler Jörg12ORCID

Affiliation:

1. Lehrstuhl für Theoretische Chemie II, Ruhr-Universität Bochum , 44780 Bochum, Germany and , 44780 Bochum, Germany

2. Research Center Chemical Sciences and Sustainability, Research Alliance Ruhr , 44780 Bochum, Germany and , 44780 Bochum, Germany

Abstract

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.

Funder

Deutsche Forschungsgemeinschaft

Publisher

AIP Publishing

Subject

Physical and Theoretical Chemistry,General Physics and Astronomy

Reference155 articles.

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