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

• Published in: Machine learning: science and technology Vol. 2; no. 2; pp. 25002 - 25020
• Main Authors: Novikov, Ivan S, Gubaev, Konstantin, Podryabinkin, Evgeny V, Shapeev, Alexander V
• Format: Journal Article
• Language: English
• Published: Bristol IOP Publishing 01.06.2021
• Subjects: Active learning; calculations; Machine learning; machine-learning interatomic potentials; Mathematical analysis; Tensors; Training
• ISSN: 2632-2153; 2632-2153
• DOI: 10.1088/2632-2153/abc9fe

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/.