activity
20242026
most citedAccelerating QM/MM simulations of electrochemical interfaces through machine learning of electronic charge densities

18 citations · 22 across the 4 of their papers we have counts for

collaborators

5 papers

physics.chem-ph2026

Kernel-Level Constant-Potential Molecular Dynamics in GROMACS

Sheng Bi, Mathieu Salanne, Jun Cheng +1

How charge rearranges within an electrode shapes the behavior of batteries, supercapacitors, and other electrochemical devices. Molecular simulations can capture this response by a…

physics.chem-ph2026

Competing Ring-Opening and Hofmann Elimination Pathways in Aqueous TEMPO Catholytes: A First-Principles Study

Line Mouaffac, Maiwenn Souetre, Guillaume Jeanmairet +1

Aqueous redox-flow batteries based on TEMPO derivatives are promising for large-scale energy storage, but their practical use is limited by the chemical instability of the oxidized…

cond-mat.mtrl-sci2025★ 4 cited

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★ 18 cited

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…