Robustness of Local Predictions in Atomistic Machine Learning Models

Machine learning (ML) models for molecules and materials commonly rely on a decomposition of the global target quantity into local, atom-centered contributions. This approach is convenient from a computational perspective, enabling large-scale ML-driven simulations with a linear-scaling cost, and al...

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Veröffentlicht in:	arXiv.org 2023-06
Hauptverfasser:	Chong, Sanggyu, Grasselli, Federico, Chiheb Ben Mahmoud, Morrow, Joe D, Deringer, Volker L, Ceriotti, Michele
Format:	Artikel
Sprache:	eng
Schlagworte:	Decomposition Machine learning Physics - Chemical Physics Physics - Materials Science Robustness Training
Online-Zugang:	Volltext
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