Publications de Michele Ceriotti

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Matsubara dynamics is the quantum-Boltzmann-conserving classical dynamics which remains when real-time coherences are taken out of the exact quantum Liouvillian [T. J. H. Hele et al., J. Chem. Phys. 142, 134103 (2015)]; because of a phase-term, it cannot b ...

A prerequisite for NMR studies of organic materials is assigning each experimental chemical shift to a set of geometrically equivalent nuclei. Obtaining the assignment experimentally can be challenging and typically requires time-consuming multidimensional ...

We study the effect of quantum vibronic coupling on the electronic properties of carbon allotropes, including molecules and solids, by combining path integral first principles molecular dynamics (FPMD) with a colored noise thermostat. In addition to avoidi ...

Nuclear Magnetic Resonance (NMR) chemical shifts are powerful probes of local atomic and electronic structure that can be used to resolve the structures of powdered or amorphous molecular solids. Chemical shift driven structure elucidation depends critical ...

Macroscopic theories of nucleation such as classical nucleation theory envision that clusters of the bulk stable phase form inside the bulk metastable phase. Molecular dynamics simulations are often used to elucidate nucleation mechanisms, by capturing the ...

The properties of molecules and materials containing light nuclei are affected by their quantum mechanical nature. Accurate modeling of these quantum nuclear effects requires computationally demanding path integral techniques. Considerable success has been ...

High-strengthmetal alloys achieve their performance via careful control of the nucleation, growth, and kinetics of precipitation. Alloy mechanical properties are then controlled by atomic scale phenomena such as shearing of the precipitates by dislocations ...

Quantifying the uncertainty of regression models is essential to ensure their reliability, particularly since their application often extends beyond their training domain. Based on the solution of a constrained optimization problem, this work proposes ‘pre ...

Machine-learning potentials are usually trained on the ground-state, Born-Oppenheimer energy surface, which depends exclusively on the atomic positions and not on the simulation temperature. This disregards the effect of thermally excited electrons, that i ...

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 ...

Due to their strong dependence on local atonic environments, NMR chemical shifts are among the most powerful tools for strucutre elucidation of powdered solids or amorphous materials. Unfortunately, using them for structure determination depends on the abi ...

Nuclear quantum effects (NQEs) cause the nuclei of light elements like hydrogen to delocalize, affecting numerous properties of water and aqueous solutions, such as hydrogen-bonding and proton transfer barriers. Here, we address the prototypical case of aq ...

The precise description of quantum nuclear fluctuations in atomistic modelling is possible by employing path integral techniques, which involve a considerable computational overhead due to the need of simulating multiple replicas of the system. Many approa ...

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 ...

In this paper, we address the challenge of obtaining a comprehensive and symmetric representation of point particle groups, such as atoms in a molecule, which is crucial in physics and theoretical chemistry. The problem has become even more important with ...

The accurate representation of multidimensional potential energy surfaces is a necessary requirement for realistic computer simulations of molecular systems. The continued increase in computer power accompanied by advances in correlated electronic structur ...

The conformational free energy landscape of aspartic acid, a proteogenic amino acid involved in a wide variety of biological functions, was investigated as an example of the complexity that multiple rotatable bonds produce even in relatively simple molecul ...

Physically motivated and mathematically robust atom-centered representations of molecular structures are key to the success of modern atomistic machine learning. They lie at the foundation of a wide range of methods to predict the properties of both materi ...

Long-range ion induced water-water correlations were recently observed in femtosecond elastic second harmonic scattering experiments of electrolyte solutions. To further the qualitative understanding of these correlations, we derive an analytical expressio ...

Michele Ceriotti

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Approximating Matsubara dynamics using the planetary model: Tests on liquid water and ice

Bayesian probabilistic assignment of chemical shifts in organic solids

Quantum vibronic effects on the electronic properties of solid and molecular carbon

A Deep Learning Model for Chemical Shieldings in Molecular Organic Solids Including Anisotropy

Bridging the gap between atomistic and macroscopic models of homogeneous nucleation

Inexpensive modeling of quantum dynamics using path integral generalized Langevin equation thermostats

Machine learning for metallurgy III: A neural network potential for Al-Mg-Si

A prediction rigidity formalism for low-cost uncertainties in trained neural networks

Predicting hot-electron free energies from ground-state data

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

Chemical shifts in molecular solids by machine learning

Nuclear Quantum Effects in Sodium Hydroxide Solutions from Neural Network Molecular Dynamics Simulations

Comment on "Water- water correlations in electrolyte solutions probed by hyper-Rayleigh scattering" [J. Chem. Phys. 147, 214505 (2017)]

High order path integrals made easy

Physics-Inspired Structural Representations for Molecules and Materials

Completeness of Atomic Structure Representations

Comparison of permutationally invariant polynomials, neural networks, and Gaussian approximation potentials in representing water interactions through many-body expansions

Mapping the conformational free energy of aspartic acid in the gas phase and in aqueous solution

Efficient implementation of atom-density representations

Communication: Mean-field theory of water-water correlations in electrolyte solutions