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cond-mat.mtrl-sci2025
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…
cond-mat.mtrl-sci2024
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…
cond-mat.mtrl-sci2024
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…