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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
Model ensembles are effective tools for estimating prediction uncertainty in deep learning atomistic force fields.
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