active-learning

Active Learning for SNAP Interatomic Potentials via Bayesian Predictive Uncertainty

Bayesian inference with a simple Gaussian error model is used to efficiently compute prediction variances for energies, forces, and stresses in the linear SNAP interatomic potential. The prediction variance is shown to have a strong correlation with …

Model Error Estimation for Active Learning of Interatomic Potential Models

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Active Learning and Uncertainty Quantification for Machine Learning Interatomic Potentials

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Earth System Model Improvement Pipeline via Uncertainty Attribution and Active Learning

Primary focal area: 2 (Predictive Modeling via AI): We develop methods to formally quantify uncertainties in Earth System models for the land-atmosphere coupled system. Science Challenge: Earth system models still have significant biases in …

Active Machine Learning with Uncertainties and Gradients

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Active Learning for Linearly Parameterized Interatomic Potentials

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