A symmetry- and gradient-enhanced Gaussian process with active learning fits rigid-rotor potential energy surfaces of gas molecules in porous environments with under 100 single-point evaluations.
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Symmetry- and Gradient-enhanced Gaussian Process Regression for the Active Learning of Potential Energy Surfaces in Porous Materials
A symmetry- and gradient-enhanced Gaussian process with active learning fits rigid-rotor potential energy surfaces of gas molecules in porous environments with under 100 single-point evaluations.