Work

The MLIP package: moment tensor potentials with MPI and active learning

Year: 2020

Type: article

Abstract: The subject of this paper is the technology (the 'how') of constructing machine-learning interatomic potentials, rather than science (the 'what' and 'why') of atomistic simulations using machine-learning potentials. Namely, we illustrate how to construct moment tensor potentials using active learning as implemented in the MLIP package, focusing on the efficient ways to automatically sample configurations for the training set, how expanding the training set changes the error of predictions, how to set up ab initio calculations in a cost-effective manner, etc. The MLIP package (short for Machine-Learning Interatomic Potentials) is available at https://mlip.skoltech.ru/download/ .

Source: Machine Learning Science and Technology

DOI: 10.1088/2632-2153/abc9fe

Authors: Ivan S Novikov, Konstantin Gubaev, Evgeny V Podryabinkin, Alexander V Shapeev

Institutions: Skolkovo Institute of Science and Technology, Delft University of Technology

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,000

Funder: Russian Science Foundation