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Found 2 papers, 1 papers with code
Information theory unifies atomistic machine learning, uncertainty quantification, and materials thermodynamics
An accurate description of information is relevant for a range of problems in atomistic modeling, such as sampling methods, detecting rare events, analyzing datasets, or performing uncertainty quantification (UQ) in machine learning (ML)-driven simulations.
LTAU-FF: Loss Trajectory Analysis for Uncertainty in Atomistic Force Fields
Though in this work we focus on the use of LTAU with deep learning atomistic force fields, we emphasize that it can be readily applied to any regression task, or any ensemble-generation technique, to provide a reliable and easy-to-implement UQ metric.
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