Scalable long-range machine learning interatomic potentials with electrical response for atomistic material modeling
Accurate atomistic simulations of materials under electric fields require long-range electrostatics and electrical-response modeling at length scales beyond the reach of conventional first-principles methods. Yet these capabilities remain difficult to reconcile with the scalability of machine-learning interatomic potentials (MLIPs). Strictly local models enable efficient spatial parallelization but neglect long-range interactions, whereas existing electrostatic extensions introduce global dependencies that hinder distributed computation. Here, we overcome this limitation by analytically separating the long-range electrostatic forces and Born effective charges (BECs) into a global electros...
| Authors | Yicheng Chen, Xiaoyun Yu, Hyejeong Song et al. |
|---|---|
| Primary category | cond-mat.mtrl-sci |
| Published | 16 October 2026 |
| Last updated at source | 16 October 2026 |
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