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Found 4 papers, 2 papers with code
An information-matching approach to optimal experimental design and active learning
These results are encouraging for diverse future applications, particularly active learning in large machine learning models.
Grand canonical generative diffusion model for crystalline phases and grain boundaries
The diffusion model has emerged as a powerful tool for generating atomic structures for materials science.
Model-free quantification of completeness, uncertainties, and outliers in atomistic machine learning using information theory
This method provides a general tool for data-driven atomistic modeling and combines efforts in ML, simulations, and physical explainability.
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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