Synthetic pre-training for neural-network interatomic potentials

Machine learning (ML) based interatomic potentials have transformed the field of atomistic materials modelling. However, ML potentials depend critically on the quality and quantity of quantum-mechanical reference data with which they are trained, and therefore developing datasets and training pipeli...

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Veröffentlicht in:	Machine learning: science and technology 2024-03, Vol.5 (1), p.15003
Hauptverfasser:	Gardner, John L A, Baker, Kathryn T, Deringer, Volker L
Format:	Artikel
Sprache:	eng
Schlagworte:	atomistic simulations Datasets Machine learning molecular dynamics Neural networks Synthetic data
Online-Zugang:	Volltext
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