4 citations · 4 across the 2 of their papers we have counts for
3 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…
Comparative study of structural and electronic properties of GaSe and InSe polytypes
Juliana Srour, Michael Badawi, Fouad El Haj Hassan +1
Equilibrium crystal structures, electron band dispersions and band gap values of layered GaSe and InSe semiconductors, each being represented by four polytypes, are studied via fir…