calorine: A Python package for constructing and sampling neuroevolution potential models
Journal article, 2024

Molecular dynamics (MD) simulations are a key tool in computational chemistry, physics, and materials science, aiding the understanding of microscopic processes but also guiding the development of novel materials. A MD simulation requires a model for the interatomic interactions. To this end, one traditionally often uses empirical interatomic potentials or force fields, which are fast but inaccurate, or ab-initio methods based on electronic structure theory such as density functional theory, which are accurate but computationally very expensive (Müser et al., 2023). Machine-learned interatomic potentials (MLIPs) have in recent years emerged as an alternative to these approaches, combining the speed of heuristic force fields with the accuracy of ab-initio techniques (Unke et al., 2021). Neuroevolution potentials (NEPs), implemented in the GPUMD package, in particular, are a highly accurate and efficient class of MLIPs (Fan et al., 2021, 2022; Fan, 2022). NEP models have already been used to study a variety of properties in a range of materials, with recent examples including radiation damage in tungsten (Liu et al., 2023), phase transitions (Fransson, Wiktor, et al., 2023) and dynamics of halide perovskites (Fransson, Rosander, et al., 2023) as well as thermal transport in two-dimensional materials (Sha et al., 2023). Here, we present calorine, a Python package that simplifies the construction, analysis and use of NEP models via GPUMD.

machine learned interaction potential

machine learning

GPUMD

molecular dynamics

Author

Eric Lindgren

Chalmers, Physics, Condensed Matter and Materials Theory

Magnus Rahm

Chalmers, Physics, Condensed Matter and Materials Theory

Erik Fransson

Chalmers, Physics, Condensed Matter and Materials Theory

Fredrik Eriksson

Chalmers, Physics, Condensed Matter and Materials Theory

Nicklas Österbacka

Chalmers, Physics, Condensed Matter and Materials Theory

Zheyong Fan

Bohai University

Paul Erhart

Chalmers, Physics, Condensed Matter and Materials Theory

Journal of Open Source Software

2475-9066 (ISSN)

Vol. 9 95 6264-6264

SwedNESS

Swedish Foundation for Strategic Research (SSF) (GSn15-0008), 2017-01-01 -- 2020-12-31.

Swedish Foundation for Strategic Research (SSF) (GSn15-0008), 2016-07-01 -- 2021-06-30.

Areas of Advance

Nanoscience and Nanotechnology

Materials Science

Roots

Basic sciences

Infrastructure

C3SE (Chalmers Centre for Computational Science and Engineering)

Subject Categories

Probability Theory and Statistics

DOI

10.21105/joss.06264

Related datasets

calorine - A Python library for building and sampling NEP models via the GPUMD package [dataset]

DOI: 10.5281/zenodo.10723374

More information

Latest update

3/25/2024