Fast and flexible range-separated models for atomistic machine learning

Most atomistic machine learning (ML) models rely on a locality ansatz, and decompose the energy into a sum of short-ranged, atom-centered contributions. This leads to clear limitations when trying to describe problems that are dominated by long-range physical effects - most notably electrostatics. M...

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Veröffentlicht in:	arXiv.org 2024-12
Hauptverfasser:	Loche, Philip, Huguenin-Dumittan, Kevin K, Honarmand, Melika, Xu, Qianjun, Rumiantsev, Egor, Wei Bin How, Langer, Marcel F, Ceriotti, Michele
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Availability Dynamic structural analysis Electrostatics Machine learning Molecular dynamics
Online-Zugang:	Volltext
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