322 citations · 335 across the 8 of their papers we have counts for
8 papers · 1 filter
SALTED: a symmetry-adapted machine-learning program for predicting electron-densities in molecules and materials
Zekun Lou, Alan M. Lewis, Théophane Bernhard +5
SALTED provides an open-source Python package for machine learning the quantum-mechanical electron density, , in molecular and condensed-phase systems based on input…
Long-range electrostatics in atomistic machine learning: a physical perspective
Federico Grasselli, Kevin Rossi, Stefano de Gironcoli +1
The inclusion of long-range electrostatics in atomistic machine learning (ML) is receiving increasing attention for achieving quantum-mechanical accuracy in predicting a wide range…
Exact Theory of Fermi-Energy Response at Metallic Interfaces
Théophane Bernhard, Andrea Grisafi
The response of the Fermi energy to external perturbations governs key physical observables at metallic interfaces. Although this response admits a local formulation in terms of th…
Roadmap on Advancements of the FHI-aims Software Package
Joseph W. Abbott, Carlos Mera Acosta, Alaa Akkoush +203
Electronic-structure theory is the foundation of the description of materials including multiscale modeling of their properties and functions. Obviously, without sufficient accurac…
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…