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Andrea Grisafi

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Related publications (13)

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Effect of a temperature gradient on the screening properties of ionic fluids

Federico Grasselli, Andrea Grisafi

The electrostatic screening properties of ionic fluids are of paramount importance in countless physical processes. Yet the screening behavior of ionic conductors out of thermal equilibrium has to date mainly been studied in the context of thermoelectric p ...

AMER PHYSICAL SOC2023

Roadmap on Machine learning in electronic structure

Michele Ceriotti, Jigyasa Nigam, Andrea Grisafi, Max David Veit

In recent years, we have been witnessing a paradigm shift in computational materials science. In fact, traditional methods, mostly developed in the second half of the XXth century, are being complemented, extended, and sometimes even completely replaced by ...

IOP Publishing Ltd2022

Theoretical and computational approaches to the study of materials and molecules have, over the last few decades, progressed at an exponential rate. Yet, the possibility of producing numerical predictions that are on par with experimental measurements is t ...

EPFL2021

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Electronic nearsightedness is one of the fundamental principles that governs the behavior of condensed matter and supports its description in terms of local entities such as chemical bonds. Locality also underlies the tremendous success of machine-learning ...

2021

Physics-Inspired Structural Representations for Molecules and Materials

Michele Ceriotti, Félix Benedito Clément Musil, Andrea Grisafi

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 its properties, involves transforming the Cartesian coordinates of the atom ...

AMER CHEMICAL SOC2021

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Machine-learning in quantum chemistry is currently booming, with reported applications spanning all molecular properties from simple atomization energies to complex mathematical objects such as the many-body wavefunction. Due to its central role in density ...

2020

Transferable Machine-Learning Model of the Electron Density

Michele Ceriotti, Alberto Fabrizio, David Mark Wilkins, Benjamin André René Meyer, Andrea Grisafi

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-structure calculations. We introduce an atom-centered, symmetry-adapted framework t ...

2019

Accurate molecular polarizabilities with coupled cluster theory and machine learning

Michele Ceriotti, David Mark Wilkins, Andrea Grisafi, Yang Yang

The molecular dipole polarizability describes the tendency of a molecule to change its dipole moment in response to an applied electric field. This quantity governs key intra- and intermolecular interactions, such as induction and dispersion; plays a vital ...

2019

Electron density learning of non-covalent systems

Michele Ceriotti, Alberto Fabrizio, Benjamin André René Meyer, Andrea Grisafi

Chemists continuously harvest the power of non-covalent interactions to control phenomena in both the micro- and macroscopic worlds. From the quantum chemical perspective, the strategies essentially rely upon an in-depth understanding of the physical origi ...

ROYAL SOC CHEMISTRY2019

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While density functional theory (DFT) is often an accurate and efficient methodology for evaluating molecular properties such as energies and multipole moments, this approach often yields larger errors for response properties such as the dipole polarizabil ...

2019