most citedUncertainty quantification design principles for machine learning interatomic potentials: lessons learned from hierarchical Bayesian inference

4 citations · 4 across the 1 of their papers we have counts for

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1 paper

cond-mat.mtrl-sci2022★ 4 cited

Uncertainty quantification design principles for machine learning interatomic potentials: lessons learned from hierarchical Bayesian inference

Albert P. Bartók, Brennon L. Shanks, James. R. Kermode +1

Reliable uncertainty quantification (UQ) remains a major challenge for machine learning interatomic potentials (MLIPs). Here, we benchmark UQ strategies for message passing neural…