Publications by Michele Ceriotti

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Machine-learning of atomic-scale properties amounts to extracting correlations between structure, composition and the quantity that one wants to predict. Representing the input structure in a way that best reflects such correlations makes it possible to im ...

Due to the subtle balance of intermolecular interactions that govern structure-property relations, predicting the stability of crystal structures formed from molecular building blocks is a highly non-trivial scientific problem. A particularly active and fr ...

Enhanced sampling techniques have become an essential tool in computational chemistry and physics, where they are applied to sample activated processes that occur on a time scale that is inaccessible to conventional simulations. Despite their popularity, i ...

At room temperature, the quantum contribution to the kinetic energy of a water molecule exceeds the classical contribution by an order of magnitude. The quantum kinetic energy (QKE) of a water molecule is modulated by its local chemical environment and lea ...

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 molecular dipole moment (mu) is a central quantity in chemistry. It is essential in predicting infrared and sum-frequency generation spectra as well as induction and long-range electrostatic interactions. Furthermore, it can be extracted directly-via t ...

Machine learning frameworks based on correlations of interatomic positions begin with a discretized description of the density of other atoms in the neighborhood of each atom in the system. Symmetry considerations support the use of spherical harmonics to ...

Materials composed of elements from the third and fifth columns of the periodic table display a very rich behavior, with the phase diagram usually containing a metallic liquid phase and a polar semiconducting solid. As a consequence, it is very hard to ach ...

Water molecules adsorbed on inorganic substrates play an important role in several technological applications. In the presence of light atoms in adsorbates, nuclear quantum effects (NQEs) influence the structural stability and the dynamical properties of t ...

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 ...

The resolving power of solid-state nuclear magnetic resonance (NMR) crystallography depends heavily on the accuracy of computational predictions of NMR chemical shieldings of candidate structures, which are usually taken to be local minima in the potential ...

Nuclear magnetic resonance (NMR) chemical shifts are a direct probe of local atomic environments and can be used to determine the structure of solid materials. However, the substantial computational cost required to predict accurate chemical shifts is a ke ...

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 ...

The interface between water and folded proteins is very complex. Proteins have "patchy" solvent-accessible areas composed of domains of varying hydrophobicity. The textbook understanding is that these domains contribute additively to interfacial properties ...

This paper explores the dynamics of a highly rotationally and vibrationally excited radical, CD2CD2OH. The radical is produced from the 193 nm photodissociation of 2-bromoethanol-d(4), so it is imparted with high angular momentum and high vibrational energ ...

A central paradigm of self-assembly is to create ordered structures starting from molecular monomers that spontaneously recognize and interact with each other via noncovalent interactions. In recent years, great efforts have been directed toward perfecting ...

Infrared pump-probe spectroscopy provides detailed information about the dynamics of hydrogen-bonded liquids. Due to dissipation of the absorbed pump pulse energy, thermal equilibration dynamics also contributes to the observed signal. Disentangling this c ...

We investigate the thermodynamics and kinetics of a hydrogen interstitial in magnetic α-iron, taking account of the quantum fluctuations of the proton as well as the anharmonicities of lattice vibrations and hydrogen hopping. We show that the diffusivity o ...

Modeling ferroelectric materials from first principles is one of the successes of density-functional theory and the driver of much development effort, requiring an accurate description of the electronic processes and the thermodynamic equilibrium that driv ...

In this paper we discuss how the information contained in atomistic simulations of homogeneous nucleation should be used when fitting the parameters in macroscopic nucleation models. We show how the number of solid and liquid atoms in such simulations can ...

Michele Ceriotti

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Feature optimization for atomistic machine learning yields a data-driven construction of the periodic table of the elements

A data-driven interpretation of the stability of organic molecular crystals

Global Free-Energy Landscapes as a Smoothly Joined Collection of Local Maps

Quantum kinetic energy and isotope fractionation in aqueous ionic solutions

Accurate molecular polarizabilities with coupled cluster theory and machine learning

Predicting molecular dipole moments by combining atomic partial charges and atomic dipoles

A smooth basis for atomistic machine learning

Modeling the Ga/As binary system across temperatures and compositions from first principles

Decisive role of nuclear quantum effects on surface mediated water dissociation at finite temperature

Incorporating long-range physics in atomic-scale machine learning

Importance of Nuclear Quantum Effects for NMR Crystallography

A Machine Learning Model of Chemical Shifts for Chemically and Structurally Diverse Molecular Solids

Multi-scale approach for the prediction of atomic scale properties

Determination and evaluation of the nonadditivity in wetting of molecularly heterogeneous surfaces

Effects of High Angular Momentum on the Unimolecular Dissociation of CD2CD2OH: Theory and Comparisons with Experiment

Identifying and Tracking Defects in Dynamic Supramolecular Polymers

Energy Relaxation and Thermal Diffusion in Infrared Pump-Probe Spectroscopy of Hydrogen-Bonded Liquids

Hydrogen Diffusion and Trapping in α-Iron: The Role of Quantum and Anharmonic Fluctuations

Thermodynamics and dielectric response of BaTiO3 by data-driven modeling

The Gibbs free energy of homogeneous nucleation: From atomistic nuclei to the planar limit