Improved uncertainty quantification for Gaussian process regression based interatomic potentials

The error estimation capability of machine learning interatomic potentials (MLIPs) based on probabilistic learning methods such as Gaussian process regression (GPR) is currently under-exploited, because of the tendancy of the predicted errors to overestimate the true error. We present approaches based on maximising either the marginal likelihood or an alternative likelihood constructed using leave-one-out cross validation to provide improved error estimates for interatomic potentials based on GPR. We benchmarked these approaches on models representing the Ar trimer, showing significant improvements in the robustness of the predicted error estimates.

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