Evaluation of uncertainty estimations for Gaussian process regression based machine learning interatomic potentials

Uncertainty estimations for machine learning interatomic potentials (MLIPs) are crucial for quantifying model error and identifying informative training samples in active learning (AL) strategies. In this study, we evaluate uncertainty estimations of Gaussian process regression (GPR)-based MLIPs, in...

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Bibliografiske detaljer

Principais autores:	Matthias Holzenkamp, Dongyu Lyu, Ulrich Kleinekathöfer, Peter Zaspel
Format:	Artigo
Sprog:	Inglês
Udgivet:	IOP Publishing 2025-01-01
Serier:	Machine Learning: Science and Technology
Fag:	uncertainty estimation Gaussian process regression machine learning interatomic potentials atomistic modeling
Online adgang:	https://doi.org/10.1088/2632-2153/ae09ef
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