Density-based long-range electrostatic descriptors for machine learning force fields
- Author(s)
- Carolin Faller, Merzuk Kaltak, Georg Kresse
- Abstract
This study presents a long-range descriptor for machine learning force fields that maintains translational and rotational symmetry, similar to short-range descriptors while being able to incorporate long-range electrostatic interactions. The proposed descriptor is based on an atomic density representation and is structurally similar to classical short-range atom-centered descriptors, making it straightforward to integrate into machine learning schemes. The effectiveness of our model is demonstrated through comparative analysis with the long-distance equivariant (LODE) [Grisafi and Ceriotti, J. Chem. Phys. 151, 204105 (2019)] descriptor. In a toy model with purely electrostatic interactions, our model achieves errors below 0.1%, worse than LODE but still very good. For real materials, we perform tests for liquid NaCl, rock salt NaCl, and solid zirconia. For NaCl, the present descriptors improve on short-range density descriptors, reducing errors by a factor of two to three and coming close to message-passing networks. However, for solid zirconia, no improvements are observed with the present approach, while message-passing networks reduce the error by almost a factor of two to three. Possible shortcomings of the present model are briefly discussed.
- Organisation(s)
- Computational Materials Physics
- External organisation(s)
- VASP Software GmbH
- Journal
- Journal of Chemical Physics
- Volume
- 161
- No. of pages
- 11
- ISSN
- 0021-9606
- DOI
- https://doi.org/10.48550/arXiv.2406.17595
- Publication date
- 12-2024
- Peer reviewed
- Yes
- Austrian Fields of Science 2012
- 103018 Materials physics, 102019 Machine learning
- Keywords
- Portal url
- https://ucrisportal.univie.ac.at/en/publications/bdee2509-0b5d-45a9-aed4-d1c276ea7cdf