New venues in electron density analysis

B Landeros-Rivera, M Gallegos, J Munárriz… - Physical Chemistry …, 2022 - pubs.rsc.org
We provide a comprehensive overview of the chemical information from electron density: not
only how to extract information, but also how to obtain and how to assess the quality of the …

Learning electron densities in the condensed phase

AM Lewis, A Grisafi, M Ceriotti… - Journal of chemical theory …, 2021 - ACS Publications
We introduce a local machine-learning method for predicting the electron densities of
periodic systems. The framework is based on a numerical, atom-centered auxiliary basis …

Predicting the charge density response in metal electrodes

A Grisafi, A Bussy, M Salanne, R Vuilleumier - Physical Review Materials, 2023 - APS
The computational study of energy storage and conversion processes calls for simulation
techniques that can reproduce the electronic response of metal electrodes under electric …

Accelerating QM/MM simulations of electrochemical interfaces through machine learning of electronic charge densities

A Grisafi, M Salanne - arXiv preprint arXiv:2405.07370, 2024 - arxiv.org
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 …

Convolutional network learning of self-consistent electron density via grid-projected atomic fingerprints

RG Lee, YH Kim - arXiv preprint arXiv:2403.19214, 2024 - arxiv.org
The self-consistent field (SCF) generation of the three-dimensional (3D) electron density
distribution ($\rho $) represents a fundamental aspect of density functional theory (DFT) and …

Learning the exciton properties of azo-dyes

S Vela, A Fabrizio, KR Briling… - The Journal of Physical …, 2021 - ACS Publications
The ab initio determination of electronic excited state (ES) properties is the cornerstone of
theoretical photochemistry. Yet, traditional ES methods become impractical when applied to …