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Self-consistent Coulomb interactions for machine learning interatomic potentials
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PRE
AI
DOI：10.1088/1361-6544/ae0402.png)
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Abstract
En 中文
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 equilibration 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.
Keywords:
Coulomb interactions
machine learning
electronic structure
tight binding
interatomic potentials
locality
body-order expansion
Journal
N
IF:
1.6
Papers:
176
Citations:
0