Improving molecular force fields across configurational space by combining supervised and unsupervised machine learning

The training set of atomic configurations is key to the performance of any Machine Learning Force Field (MLFF) and, as such, the training set selection determines the applicability of the MLFF model for predictive molecular simulations. However, most atomistic reference datasets are inhomogeneously...

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Veröffentlicht in:	The Journal of chemical physics 2021-03, Vol.154 (12), p.124102-124102
Hauptverfasser:	Fonseca, Gregory, Poltavsky, Igor, Vassilev-Galindo, Valentin, Tkatchenko, Alexandre
Format:	Artikel
Sprache:	eng
Schlagworte:	Alanine Artificial neural networks Configurations Datasets Equilibrium Machine learning Model accuracy Organic chemistry Physics Training Unsupervised learning
Online-Zugang:	Volltext
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