128 citations · 132 across the 3 of their papers we have counts for
4 papers
Assessing the Accuracy of Machine Learning Thermodynamic Perturbation Theory: Density Functional Theory and Beyond
Basile Herzog, Mauricio Chagas da Silva, Bastien Casier +5
Machine learning thermodynamic perturbation theory (MLPT) is a promising approach to compute finite temperature properties when the goal is to compare several different levels of a…
Hybrid localized graph kernel for machine learning energy-related properties of molecules and solids
Bastien Casier, Mauricio Chagas da Silva, Michael Badawi +4
Nowadays, the coupling of electronic structure and machine learning techniques serves as a powerful tool to predict chemical and physical properties of a broad range of systems. Wi…
A fractionally ionic approach to polarizability and van der Waals many-body dispersion calculations
Tim Gould, Sébastien Lebègue, János G. Ángyán +1
By explicitly including fractionally ionic contributions to the polarizability of a many-component system we are able to significantly improve on previous atom-wise many-body van d…
coefficients and dipole polarizabilities for all atoms and many ions in rows 1-6 of the periodic table
Tim Gould, Tomas Bucko
Using time-dependent density functional theory (tdDFT) with exchange kernels we calculate and test imaginary frequency-dependent dipole polarizabilities for all atoms and many ns i…