18 citations · 22 across the 4 of their papers we have counts for
3 papers · 1 filter
Learning the Electrostatic Response of the Electron Density through a Symmetry-Adapted Vector Field Model
Mariana Rossi, Kevin Rossi, Alan M. Lewis +2
A current challenge in atomistic machine learning is that of efficiently predicting the response of the electron density under electric fields. We address this challenge with symme…
Accelerating QM/MM simulations of electrochemical interfaces through machine learning of electronic charge densities
Andrea Grisafi, Mathieu Salanne
A crucial aspect in the simulation of electrochemical interfaces consists in treating the distribution of electronic charge of electrode materials that are put in contact with an e…
Accounting for the Quantum Capacitance of Graphite in Constant Potential Molecular Dynamics Simulations
Kateryna Goloviznina, Johann Fleischhaker, Tobias Binninger +6
Molecular dynamics simulations at a constant electric potential are an essential tool to study electrochemical processes, providing microscopic information on the structural, therm…