Self-consistent Coulomb interactions for machine learning interatomic potentials
Abstract
A ubiquitous approach to obtain transferable machine learning-based models of potential energy surfaces for atomistic systems is to decompose the total energy into a sum of local atom-centred contributions. However, in many systems non-negligible long-range electrostatic effects must be taken into account as well. We introduce a general mathematical framework to study how such long-range effects can be included in a way that (i) allows charge equilibration and (ii) retains the locality of the learnable atom-centred contributions to ensure transferability. Our results give partial explanations for the success of existing machine learned potentials that include equilibriation and provide perspectives how to design such schemes in a systematic way. To complement the rigorous theoretical results, we describe a practical scheme for fitting the energy and electron density of water clusters.
- Publication:
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arXiv e-prints
- Pub Date:
- June 2024
- DOI:
- 10.48550/arXiv.2406.10915
- arXiv:
- arXiv:2406.10915
- Bibcode:
- 2024arXiv240610915T
- Keywords:
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- Physics - Computational Physics;
- Condensed Matter - Materials Science;
- 65E05;
- 74E15;
- 81V45;
- 81V70
- E-Print:
- 33 pages, 1 figure