Publications by Andrea Grisafi

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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 ...

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 material are written as a sum over contributions that depend on the configu ...

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 ...

Second-harmonic scattering (SHS) experiments provide a unique approach to probe noncentrosymmetric environments in aqueous media, from bulk solutions to interfaces, living cells, and tissue. A central assumption made in analyzing SHS experiments is that ea ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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, which enables an accurate expansion of the all-electron density in a form suitable ...

Vibrational properties of molecular crystals are constantly used as structural fingerprints, in order to identify both the chemical nature and the structural arrangement of molecules. The simulation of these properties is typically very costly, especially ...

Andrea Grisafi

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Accurate molecular polarizabilities with coupled cluster theory and machine learning

Incorporating long-range physics in atomic-scale machine learning

Multi-scale approach for the prediction of atomic scale properties

Solvent fluctuations and nuclear quantum effects modulate the molecular hyperpolarizability of water

Physics-enhanced machine learning with symmetry-adapted and long-range representations

Transferable Machine-Learning Model of the Electron Density

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

Quantum mechanical static dipole polarizabilities in the QM7b and AlphaML showcase databases

Physics-Inspired Structural Representations for Molecules and Materials

Electron density learning of non-covalent systems

Learning Electron Densities in the Condensed Phase

Using Gaussian process regression to simulate the vibrational Raman spectra of molecular crystals

Learning (from/about) the ground-state electron density

Symmetry-Adapted Machine Learning for Tensorial Properties of Atomistic Systems