117 citations · 1.1k across the 40 of their papers we have counts for
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stat.CO2020★ 36 cited
Random Sampling High Dimensional Model Representation Gaussian Process Regression (RS-HDMR-GPR) for representing multidimensional functions with machine-learned lower-dimensional terms allowing insight with a general method
Owen Ren, Mohamed Ali Boussaidi, Dmitry Voytsekhovsky +2
We present a Python implementation for RS-HDMR-GPR (Random Sampling High Dimensional Model Representation Gaussian Process Regression). The method builds representations of multiva…
cond-mat.mtrl-sci2020★ 22 cited
Non-parametric Local Pseudopotentials with Machine Learning: a Tin Pseudopotential Built Using Gaussian Process Regression
Johann Lueder, Sergei Manzhos
We present novel non-parametric representation math for local pseudopotentials (PP) based on Gaussian Process Regression (GPR). Local pseudopotentials are needed for materials simu…
physics.comp-ph2020★ 46 cited
Data-driven kinetic energy density fitting for orbital-free DFT: linear vs Gaussian process regression
Sergei Manzhos, Pavlo Golub
We study the dependence of kinetic energy densities (KED) on density-dependent variables that have been suggested in previous works on kinetic energy functionals (KEF) for orbital-…