Home

Person

Michele Ceriotti

This person has left EPFL

About
Privacy
Disclaimer

Graph Chatbot

Related publications (160)

Page 5 of 8

Computing the absolute Gibbs free energy in atomistic simulations: Applications to defects in solids

Michele Ceriotti, Bingqing Cheng

2018

Machine Learning at the Atomic Scale

Michele Ceriotti, Félix Benedito Clément Musil

Statistical learning algorithms are finding more and more applications in science and technology. Atomic-scale modeling is no exception, with machine learning becoming commonplace as a tool to predict energy, forces and properties of molecules and condense ...

SWISS CHEMICAL SOC2019

Machine-Learning of Atomic-Scale Properties Based on Physical Principles

Michele Ceriotti, Michael John Willatt

We briefly summarize the kernel regression approach, as used recently in materials modelling, to fitting functions, particularly potential energy surfaces, and highlight how the linear algebra framework can be used to both predict and train from linear fun ...

Springer International Publishing2020

Chemiscope: interactive structure-property explorer for materials and molecules

Michele Ceriotti, Guillaume André Jean Fraux

The number of materials or molecules that can be created by combining different chemical elements in various proportions and spatial arrangements is enormous. Computational chemistry can be used to generate databases containing billions of potential struct ...

2020

Atom-density representations for machine learning

Michele Ceriotti, Félix Benedito Clément Musil, Michael John Willatt

This has led to a proliferation of alternative ways to convert an atomic structure into an input for a machine-learning model. We introduce an abstract definition of chemical environments that is based on a smoothed atomic density, using a bra-ket notation ...

2019

Evaluating functions of positive-definite matrices using colored-noise thermostats

Michele Ceriotti

Many applications in computational science require computing the elements of a function of a large matrix. A commonly used approach is based on the the evaluation of the eigenvalue decomposition, a task that, in general, involves a computing time that scal ...

Amer Physical Soc2014

Ab initio Modelling of the Early Stages of Precipitation in Al-6000 Alloys

Michele Ceriotti, William Curtin, Till Junge, Daniele Giofré

Age hardening induced by the formation of (semi)-coherent precipitate phases is crucial for the processing and final properties of the widely used Al-6000 alloys. Early stages of precipitation are particularly important from the fundamental and technologic ...

Elsevier2017

Thermal conductivity of Li 3 PS 4 solid electrolytes with ab initio accuracy

Michele Ceriotti, Federico Grasselli, Davide Tisi, Lorenzo Gigli

The vast amount of computational studies on electrical conduction in solid-state electrolytes is not mirrored by comparable efforts addressing thermal conduction, which has been scarcely investigated despite its relevance to thermal management and (over)he ...

Amer Physical Soc2024

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

Michele Ceriotti, Michael John Willatt

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 ...

2018

Bayesian probabilistic assignment of chemical shifts in organic solids

David Lyndon Emsley, Michele Ceriotti, Manuel Cordova, Bruno Simões de Almeida, Martins Balodis

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 ...

2021

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

Michele Ceriotti

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 ...

AMER PHYSICAL SOC2021

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

Florian Mathieu Pascal Viscosi, Ruben Rodriguez Madrid, David Lyndon Emsley, Michele Ceriotti, Matthias Linus Kellner, Jacob Brian Holmes, Yuxuan Zhang

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 ...

AMER CHEMICAL SOC2025

Bridging the gap between atomistic and macroscopic models of homogeneous nucleation

Michele Ceriotti, Bingqing Cheng

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 ...

American Institute of Physics2017

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

Michele Ceriotti, David Mark Wilkins, Venkat Kapil, Jinggang Lan

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 ...

2020

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

Michele Ceriotti, William Curtin, Daniel John Gilles Marchand, Albert Johann Glensk, Abhinav Jain

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 ...

2021

Anisotropy of the Proton Momentum Distribution in Water

Michele Ceriotti, Venkat Kapil

2018

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

Michele Ceriotti, Federico Grasselli, Sanggyu Chong, Filippo Bigi

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 ...

2024

Predicting hot-electron free energies from ground-state data

Michele Ceriotti, Federico Grasselli, Chiheb Ben Mahmoud

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 ...

AMER PHYSICAL SOC2022

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

Michele Ceriotti, David Mark Wilkins, Andrea Grisafi

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 ...

2019

Chemical shifts in molecular solids by machine learning

David Lyndon Emsley, Michele Ceriotti, Félix Benedito Clément Musil, Albert Hofstetter, Sandip De, Federico Maria Paruzzo, Maxime Lagier

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 ...

Nature Research2018