25 citations · 41 across the 6 of their papers we have counts for
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physics.chem-ph2023
Neural network Gaussian processes as efficient models of potential energy surfaces for polyatomic molecules
Jun Dai, Roman V. Krems
Kernel models of potential energy surfaces (PES) for polyatomic molecules are often restricted by a specific choice of the kernel function. This can be avoided by optimizing the co…
physics.chem-ph2020★ 25 cited
Machine-learning-corrected quantum dynamics calculations
A. Jasinski, J. Montaner, R. C. Forrey +6
Quantum scattering calculations for all but low-dimensional systems at low energies must rely on approximations. All approximations introduce errors. The impact of these errors is…
physics.chem-ph2019
Interpolation and extrapolation of global potential energy surfaces for polyatomic systems by Gaussian processes with composite kernels
Jun Dai, Roman V. Krems
Gaussian process regression has recently emerged as a powerful, system-agnostic tool for building global potential energy surfaces (PES) of polyatomic molecules. While the accuracy…