De novo exploration and self-guided learning of potential-energy surfaces
Publication Date
2019-12-01Journal Title
npj Computational Materials
ISSN
2057-3960
Publisher
Springer Nature
Volume
5
Issue
1
Type
Article
This Version
AM
Metadata
Show full item recordCitation
Bernstein, N., Csányi, G., & Deringer, V. (2019). De novo exploration and self-guided learning of potential-energy surfaces. npj Computational Materials, 5 (1)https://doi.org/10.1038/s41524-019-0236-6
Abstract
Interatomic potential models based on machine learning (ML) are rapidly developing as tools for materials 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.
Sponsorship
Isaac Newton Trust (17.08(c))
Leverhulme Trust (ECF-2017-278)
EPSRC (VIA UNIVERSITY OF OXFORD) (EP/L014742/1)
EPSRC (EP/P022596/1)
Embargo Lift Date
2022-09-11
Identifiers
External DOI: https://doi.org/10.1038/s41524-019-0236-6
This record's URL: https://www.repository.cam.ac.uk/handle/1810/296772
Rights
All rights reserved