Efficient and accurate machine-learning interpolation of atomic energies in compositions with many species

Nongnuch Artrith*, Alexander Urban, Gerbrand Ceder

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

232 Citations (Scopus)

Abstract

Machine-learning potentials (MLPs) for atomistic simulations are a promising alternative to conventional classical potentials. Current approaches rely on descriptors of the local atomic environment with dimensions that increase quadratically with the number of chemical species. In this paper, we demonstrate that such a scaling can be avoided in practice. We show that a mathematically simple and computationally efficient descriptor with constant complexity is sufficient to represent transition-metal oxide compositions and biomolecules containing 11 chemical species with a precision of around 3 meV/atom. This insight removes a perceived bound on the utility of MLPs and paves the way to investigate the physics of previously inaccessible materials with more than ten chemical species.

Original languageEnglish
Article number014112
JournalPhysical Review B
Volume96
Issue number1
DOIs
Publication statusPublished - 21 Jul 2017

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