Uncertainty Driven Active Learning of Coarse Grained Free Energy Models

Coarse graining techniques play an essential role in accelerating molecular simulations of systems with large length and time scales. Theoretically grounded bottom-up models are appealing due to their thermodynamic consistency with the underlying all-atom models. In this direction, machine learning...

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Veröffentlicht in:	arXiv.org 2022-10
Hauptverfasser:	Duschatko, Blake R, Vandermause, Jonathan, Molinari, Nicola, Kozinsky, Boris
Format:	Artikel
Sprache:	eng
Schlagworte:	Active learning Data collection Free energy Granulation Machine learning Model accuracy Training Uncertainty
Online-Zugang:	Volltext
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