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Michele Ceriotti

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Related publications (160)

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

Michele Ceriotti, Alexander Jan Goscinski, Lorenzo Gigli

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 ...

2024

How to remove the spurious resonances from ring polymer molecular dynamics

Michele Ceriotti, Mariana Rossi Carvalho

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 ...

Amer Inst Physics2014

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

Michele Ceriotti, Piero Gasparotto, Benjamin Aaron Helfrecht, Federico Giberti

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 ...

2019

Recursive evaluation and iterative contraction of N-body equivariant features

Michele Ceriotti, Jigyasa Nigam, Sergey Pozdnyakov

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 ...

2020

Gaussian Process Regression for Materials and Molecules

Michele Ceriotti, David Mark Wilkins

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 ...

AMER CHEMICAL SOC2021

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

Michele Ceriotti, Bingqing Cheng

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 ...

AMER INST PHYSICS2020

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

Michele Ceriotti, Sylvie Roke, David Mark Wilkins, Andrea Grisafi, Gabriele Tocci, Chung-Wen Liang

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 ...

Amer Physical Soc2017

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

Michele Ceriotti, Bingqing Cheng

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 ...

2018

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

Michele Ceriotti, Mariana Rossi Carvalho

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 ...

Amer Chemical Soc2016

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

Michele Ceriotti, Federico Giberti

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 ...

Amer Chemical Soc2014

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

Michele Ceriotti, Fabio Pietrucci

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 ...

AMER CHEMICAL SOC2020

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

Michele Ceriotti, Venkat Kapil

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 ...

AMER CHEMICAL SOC2020

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

Michele Ceriotti, Sylvie Roke, David Mark Wilkins, Gabriele Tocci, Chung-Wen Liang

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 ...

Amer Chemical Soc2016

Structure-property maps with Kernel principal covariates regression

Michele Ceriotti, Guillaume André Jean Fraux, Benjamin Aaron Helfrecht, Rose Kathleen Cersonsky-Adorf

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 ...

2020

Transferable Machine-Learning Model of the Electron Density

Michele Ceriotti, Anne-Clémence Corminboeuf, 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

Iterative Unbiasing of Quasi-Equilibrium Sampling

Michele Ceriotti, Bingqing Cheng, Federico Giberti

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 ...

AMER CHEMICAL SOC2020

Fast and Accurate Uncertainty Estimation in Chemical Machine Learning

Michele Ceriotti, Félix Benedito Clément Musil, Michael John Willatt, Mikhail Langovoy

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 ...

2019

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

Michele Ceriotti, Venkat Kapil

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 ...

2019

Physics-Inspired Equivariant Descriptors of Nonbonded Interactions

Michele Ceriotti, Philip Robin Loche, Kevin Kazuki Huguenin-Dumittan

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 ...

Amer Chemical Soc2023

Roadmap on Machine learning in electronic structure

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

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