522 citations · 1.7k across the 18 of their papers we have counts for
19 papers
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 Electrostatic 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…
Predicting the Charge Density Response in Metal Electrodes
Andrea Grisafi, Augustin Bussy, Mathieu Salanne +1
The computational study of energy storage and conversion processes calls for simulation techniques that can reproduce the electronic response of metal electrodes under electric fie…
Effect of a Temperature Gradient on the Screening Properties of Ionic Fluids
Andrea Grisafi, Federico Grasselli
The electrostatic screening properties of ionic fluids are of paramount importance in countless physical processes. Yet, the behavior of ionic conductors out of thermal equilibrium…
Electronic-structure properties from atom-centered predictions of the electron density
Andrea Grisafi, Alan M. Lewis, Mariana Rossi +1
The electron density of a molecule or material has recently received major attention as a target quantity of machine-learning models. A natural choice to construct a model that yie…
Learning electron densities in the condensed phase
Alan M. Lewis, Andrea Grisafi, Michele Ceriotti +1
We introduce a local machine-learning method for predicting the electron densities of periodic systems. The framework is based on a numerical, atom-centred auxiliary basis, which e…
Physics-inspired structural representations for molecules and materials
Felix Musil, Andrea Grisafi, Albert P. Bartók +3
The first step in the construction of a regression model or a data-driven analysis, aiming to predict or elucidate the relationship between the atomic scale structure of matter and…
Multi-scale approach for the prediction of atomic scale properties
Andrea Grisafi, Jigyasa Nigam, Michele Ceriotti
Electronic nearsightedness is one of the fundamental principles governing the behavior of condensed matter and supporting its description in terms of local entities such as chemica…
Incorporating long-range physics in atomic-scale machine learning
Andrea Grisafi, Michele Ceriotti
The most successful and popular machine learning models of atomic-scale properties derive their transferability from a locality ansatz. The properties of a large molecule or a bulk…
Using Gaussian Process Regression to Simulate the Vibrational Raman Spectra of Molecular Crystals
Nathaniel Raimbault, Andrea Grisafi, Michele Ceriotti +1
Vibrational properties of molecular crystals are constantly used as structural fingerprints, in order to identify both the chemical nature and the structural arrangement of molecul…
Atomic-scale representation and statistical learning of tensorial properties
Andrea Grisafi, David M. Wilkins, Michael J. Willatt +1
This chapter discusses the importance of incorporating three-dimensional symmetries in the context of statistical learning models geared towards the interpolation of the tensorial…
A Transferable Machine-Learning Model of the Electron Density
Andrea Grisafi, David M. Wilkins, Benjamin A. R. Meyer +3
The electronic charge density plays a central role in determining the behavior of matter at the atomic scale, but its computational evaluation requires demanding electronic-structu…
Accurate molecular polarizabilities with coupled-cluster theory and machine learning
David M. Wilkins, Andrea Grisafi, Yang Yang +3
The molecular polarizability describes the tendency of a molecule to deform or polarize in response to an applied electric field. As such, this quantity governs key intra- and inte…
Symmetry-Adapted Machine-Learning for Tensorial Properties of Atomistic Systems
Andrea Grisafi, David M. Wilkins, Gábor Csányi +1
Statistical learning methods show great promise in providing an accurate prediction of materials and molecular properties, while minimizing the need for computationally demanding e…
Solvent Fluctuations and Nuclear Quantum Effects Modulate the Molecular Hyperpolarizability of Water
Chungwen Liang, Gabriele Tocci, David Wilkins +3
Second-Harmonic Scatteringh (SHS) experiments provide a unique approach to probe non-centrosymmetric environments in aqueous media, from bulk solutions to interfaces, living cells…