Publications by Michele Ceriotti

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The accurate description of the structural and thermodynamic properties of ferroelectrics has been one of the most remarkable achievements of density functional theory (DFT). However, running large simulation cells with DFT is computationally demanding, wh ...

Two of the most successful methods that are presently available for simulating the quantum dynamics of condensed phase systems are centroid molecular dynamics (CMD) and ring polymer molecular dynamics (RPMD). Despite their conceptual differences, practical ...

Rationalizing the structure and structure–property relations for complex materials such as polymers or biomolecules relies heavily on the identification of local atomic motifs, e.g., hydrogen bonds and secondary structure patterns, that are seen as buildin ...

Mapping an atomistic configuration to a symmetrized N-point correlation of a field associated with the atomic positions (e.g., an atomic density) has emerged as an elegant and effective solution to represent structures as the input of machine-learning algo ...

We provide an introduction to Gaussian process regression (GPR) machinelearning methods in computational materials science and chemistry. The focus of the present review is on the regression of atomistic properties: in particular, on the construction of in ...

Macroscopic models of nucleation provide powerful tools for understanding activated phase transition processes. These models do not provide atomistic insights and can thus sometimes lack material-specific descriptions. Here, we provide a comprehensive fram ...

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 ...

Estimating the homogeneous ice nucleation rate from undercooled liquid water is crucial for understanding many important physical phenomena and technological applications, and challenging for both experiments and theory. From a theoretical point of view, d ...

The diffusion of protons and hydroxide ions along water wires provides an efficient mechanism for charge transport that is exploited by biological membrane channels and shows promise for technological applications such as fuel cells. However, what is lacki ...

In this work, we revisit the role of nuclear quantum effects on the structural and electronic properties of the excess proton in bulk liquid water using advanced molecular dynamics techniques. The hydronium ion is known to be a weak acceptor of a hydrogen ...

Machine learning encompasses tools and algorithms that are now becoming popular in almost all scientific and technological fields. This is true for molecular dynamics as well, where machine learning offers promises of extracting valuable information from t ...

Imaginary time path-integral (PI) simulations that account for nuclear quantum effects (NQE) beyond the harmonic approximation are increasingly employed together with modern electronic-structure calculations. Existing PI methods are applicable to molecules ...

econd-harmonic scattering experiments of water and other bulk molecular liquids have long been assumed to be insensitive to interactions between the molecules. The measured intensity is generally thought to arise from incoherent scattering due to individua ...

Data analyses based on linear methods constitute the simplest, most robust, and transparent approaches to the automatic processing of large amounts of data for building supervised or unsupervised machine learning models. Principal covariates regression (PC ...

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 ...

Atomistic modeling of phase transitions, chemical reactions, or other rare events that involve overcoming high free energy barriers usually entails prohibitively long simulation times. Introducing a bias potential as a function of an appropriately chosen s ...

We present a scheme to obtain an inexpensive and reliable estimate of the uncertainty associated with the predictions of a machine-learning model of atomic and molecular properties. The scheme is based on resampling, with multiple models being generated ba ...

Metal–organic frameworks show both fundamental interest and great promise for applications in adsorption-based technologies, such as the separation and storage of gases. The flexibility and complexity of the molecular scaffold pose a considerable challenge ...

One essential ingredient in many machine learning (ML) based methods for atomistic modeling of materials and molecules is the use of locality. While allowing better system-size scaling, this systematically neglects long-range (LR) effects such as electrost ...

Data-driven techniques are increasingly used to replace electronic-structure calculations of matter. In this context, a relevant question is whether machine learning (ML) should be applied directly to predict the desired properties or combined explicitly w ...

Michele Ceriotti

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Modeling the ferroelectric phase transition in barium titanate with DFT accuracy and converged sampling

How to remove the spurious resonances from ring polymer molecular dynamics

Atomic Motif Recognition in (Bio)Polymers: Benchmarks From the Protein Data Bank

Recursive evaluation and iterative contraction of N-body equivariant features

Gaussian Process Regression for Materials and Molecules

Classical nucleation theory predicts the shape of the nucleus in homogeneous solidification

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

Theoretical prediction of the homogeneous ice nucleation rate: disentangling thermodynamics and kinetics

Nuclear Quantum Effects in H+ and OH- Diffusion along Confined Water Wires

The Role of Quantum Effects on Structural and Electronic Fluctuations in Neat and Charged Water

Machine Learning Force Fields and Coarse-Grained Variables in Molecular Dynamics: Application to Materials and Biological Systems

Accurate Description of Nuclear Quantum Effects with High-Order Perturbed Path Integrals (HOPPI)

Second-Harmonic Scattering as a Probe of Structural Correlations in Liquids

Structure-property maps with Kernel principal covariates regression

Transferable Machine-Learning Model of the Electron Density

Iterative Unbiasing of Quasi-Equilibrium Sampling

Fast and Accurate Uncertainty Estimation in Chemical Machine Learning

Modeling the Structural and Thermal Properties of Loaded Metal–Organic Frameworks. An Interplay of Quantum and Anharmonic Fluctuations

Physics-Inspired Equivariant Descriptors of Nonbonded Interactions

Electronic Excited States from Physically Constrained Machine Learning