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The interpolation of high-dimensional potential energy surfaces (PESs) iscommonly done with physically-inspired deep-neural network models. In thiswork, we illustrate that Gaussian Processes (GPs) are also capable ofinterpolating high-dimensional complex physical systems. The accuracy of GPsdepends on the robustness of the kernel function, and a boost in the accuracyis achieved by linearly combining kernel functions. In this work, we proposedan alternative route by parametrizing the kernel function through Bochners'theorem. We interpolated the PES of various chemical systems achieving a globalaccuracy of < 0.06 kcal/mol for Benzene, Malonaldehyde, Ethanol, and protonatedImidazole dimer using only 15 000 training points. Additionally, for Aspirin,we achieved a global error of 0.063 kcal/mol with 20 000 points. Given theseresults, we believe this kernel function is system-agnostic and could allow GPsto tackle a wider variety of high-dimensional physical systems.

Gaussian Processes with Spectral Delta kernel for higher accurate Potential Energy surfaces for large molecules