273 citations · 338 across the 6 of their papers we have counts for
6 papers
SPAM(a,b): encoding the density information from guess Hamiltonian in quantum machine learning representations
Ksenia R. Briling, Yannick Calvino Alonso, Alberto Fabrizio +1
Recently, we introduced a class of molecular representations for kernel-based regression methods -- the spectrum of approximated Hamiltonian matrices (SPAM) -- that ta…
SPAM: the Spectrum of Approximated Hamiltonian Matrices representations
Alberto Fabrizio, Ksenia R. Briling, Clemence Corminboeuf
Physics-inspired molecular representations are the cornerstone of similarity-based learning applied to solve chemical problems. Despite their conceptual and mathematical diversity,…
Impact of quantum-chemical metrics on the machine learning prediction of electron density
Ksenia R. Briling, Alberto Fabrizio, Clemence Corminboeuf
Machine learning (ML) algorithms have undergone an explosive development impacting every aspect of computational chemistry. To obtain reliable predictions, one needs to maintain th…
Learning on-top: regressing the on-top pair density for real-space visualization of electron correlation
Alberto Fabrizio, Ksenia R. Briling, David D. Girardier +1
The on-top pair density [] is a local quantum-chemical property that reflects the probability of two electrons of any spin to occupy the same position in sp…
Learning the energy curvature versus particle number in approximate density functionals
Alberto Fabrizio, Benjamin Meyer, Clemence Corminboeuf
The average energy curvature as a function of the particle number is a molecule-specific quantity, which measures the deviation of a given functional from the exact conditions of d…
A Transferable Machine-Learning Model of the Electron Density
Andrea Grisafi, David M. Wilkins, Benjamin A. R. Meyer +3
The electronic charge density plays a central role in determining the behavior of matter at the atomic scale, but its computational evaluation requires demanding electronic-structu…