Accession Number:

AD1096666

Title:

De Novo Exploration and Self-Guided Learning of Potential-Energy Surfaces

Descriptive Note:

Journal Article - Open Access

Corporate Author:

NAVAL RESEARCH LAB WASHINGTON DC WASHINGTON United States

Report Date:

2019-10-11

Pagination or Media Count:

9.0

Abstract:

Interatomic potential models based on machine learning ML are rapidly developing as tools for material simulations. However, because of their flexibility, they require large fitting databases that are normally created with substantial manual selection and tuning of reference configurations. Here, we show that ML potentials can be built in a largely automated fashion, exploring and fitting potential-energy surfaces from the beginning de novo within one and the same protocol. The key enabling step is the use of a configuration-averaged kernel metric that allows one to select the few most relevant and diverse structures at each step. The resulting potentials are accurate and robust for the wide range of configurations that occur during structure searching, despite only requiring a relatively small number of single-point DFT calculations on small unit cells. We apply the method to materials with diverse chemical nature and coordination environments, marking an important step toward the more routine application of ML potentials in physics, chemistry, and materials science.

Subject Categories:

Distribution Statement:

APPROVED FOR PUBLIC RELEASE