most citedModel-free quantification of completeness, uncertainties, and outliers in atomistic machine learning using information theory

29 citations · 44 across the 4 of their papers we have counts for

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cs.LG2024

LTAU-FF: Loss Trajectory Analysis for Uncertainty in Atomistic Force Fields

Joshua A. Vita, Amit Samanta, Fei Zhou +1

Model ensembles are effective tools for estimating prediction uncertainty in deep learning atomistic force fields. However, their widespread adoption is hindered by high computatio…