Michele Ceriotti

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michele.ceriotti@epfl.ch +41 21 693 29 39 http://cosmo.epfl.ch/

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+41 21 693 29 39
EPFL > STI > STI-SMX > SMX-ENS

+41 21 693 29 39
EPFL > VPA-AVP-PGE > AVP-PGE-EDOC > EDMX-ENS

EPFL STI IMX COSMO
MXG 337 (Bâtiment MXG)
Station 12
1015 Lausanne

+41 21 693 29 39
Office: MXG 337
EPFL > STI > IMX > COSMO

Web site: Web site: https://cosmo.epfl.ch/

EPFL SCITAS-CD
PH A2 364 (Bâtiment PH)
Station 3
1015 Lausanne

+41 21 693 29 39
Office: PH A2 364
EPFL > VPA-AVP-CP > SCITAS > SCITAS-CD

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Fields of expertise

Atomistic computer simulations, statistical mechanics, machine learning, molecular dynamics, nuclear quantum effects, molecular materials, nucleation.

Biography

Michele Ceriotti received his Ph.D. in Physics from ETH Zürich. He spent three years in Oxford as a Junior Research Fellow at Merton College. Since 2013 he leads the laboratory for Computational Science and Modeling, in the institute of Materials at EPFL, that focuses on method development for atomistic materials modeling based on statistical mechanics and machine learning. He is one of the core developers of several open-source software packages, including http://ipi-code.org and http://chemiscope.org, and proudly serves the atomistic modeling community as an associate editor of the Journal of Chemical Physics, as a moderator of the physics.chem-ph section of the arXiv, and as an editorial board member of Physical Review Materials.

Publications

Infoscience publications

2024

Surface segregation in high-entropy alloys from alchemical machine learning

A. Mazitov; M. A. Springer; N. Lopanitsyna; G. Fraux; S. De et al.

Journal Of Physics-Materials. 2024-04-01. DOI : 10.1088/2515-7639/ad2983.

Michele Ceriotti

Fields of expertise

Biography

Publications

Infoscience publications

2024

Surface segregation in high-entropy alloys from alchemical machine learning

Efficient and insightful descriptors for representing molecular and material space

2023

Accelerated chemical science with AI

Natural aging and vacancy trapping in Al-6xxx

Physics-Inspired Equivariant Descriptors of Nonbonded Interactions

Robustness of Local Predictions in Atomistic Machine Learning Models

Fast evaluation of spherical harmonics with sphericart

Modelling of metal alloys in realistic conditions with machine learning

Modeling high-entropy transition metal alloys with alchemical compression

Machine-learning the electronic density of states: electronic properties without quantum mechanics

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

2022

Ranking the synthesizability of hypothetical zeolites with the sorting hat

Roadmap on Machine learning in electronic structure

A smooth basis for atomistic machine learning

Beyond potentials: Integrated machine learning models for materials

Incompleteness of graph neural networks for points clouds in three dimensions

Comment on "Manifolds of quasi-constant SOAP and ACSF fingerprints and the resulting failure to machine learn four-body interactions" [J. Chem. Phys. 156, 034302 (2022)]

Predicting hot-electron free energies from ground-state data

Thermodynamics and dielectric response of BaTiO3 by data-driven modeling

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

Characterization and prediction of peptide structures on inorganic surfaces

Unified theory of atom-centered representations and message-passing machine-learning schemes

Local Kernel Regression and Neural Network Approaches to the Conformational Landscapes of Oligopeptides

Equivariant representations for molecular Hamiltonians and N-center atomic-scale properties

2021

Local invertibility and sensitivity of atomic structure-feature mappings

2020 JCP Emerging Investigator Special Collection

Reply to: On the liquid-liquid phase transition of dense hydrogen

Learning Electron Densities in the Condensed Phase

Bayesian probabilistic assignment of chemical shifts in organic solids

Transferable machine-learning models of complex materials: the case of GaAs

Optimal radial basis for density-based atomic representations

Introduction: Machine Learning at the Atomic Scale

Gaussian Process Regression for Materials and Molecules

Chemical physics software

Physics-Inspired Structural Representations for Molecules and Materials

Importance of Nuclear Quantum Effects for NMR Crystallography

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

Improving sample and feature selection with principal covariates regression

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

Structure-Property Relationships in Complex Materials by Combining Supervised and Unsupervised Machine Learning

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

Physics-enhanced machine learning with symmetry-adapted and long-range representations

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

The role of feature space in atomistic learning

Machine learning meets chemical physics

Finite-temperature materials modeling from the quantum nuclei to the hot electron regime

A general and efficient framework for atomistic machine learning

Uncertainty estimation for molecular dynamics and sampling

Efficient implementation of atom-density representations

Simulating the ghost: quantum dynamics of the solvated electron

Multi-scale approach for the prediction of atomic scale properties

Origins of structural and electronic transitions in disordered silicon

2020

Structure-property maps with Kernel principal covariates regression

Learning the electronic density of states in condensed matter

Characterising Structure and Stability of Materials using Machine Learning

Incompleteness of Atomic Structure Representations

Gas-sieving zeolitic membranes fabricated by condensation of precursor nanosheets

Evidence for supercritical behaviour of high-pressure liquid hydrogen

Chemiscope: interactive structure-property explorer for materials and molecules

Recursive evaluation and iterative contraction of N-body equivariant features

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

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

Simulating Solvation and Acidity in Complex Mixtures with First-Principles Accuracy: The Case of CH3SO3H and H2O2 in Phenol

Nuclear Quantum Effects: Fast and Accurate

3D Ordering at the Liquid-Solid Polar Interface of Nanowires

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

Quantum kinetic energy and isotope fractionation in aqueous ionic solutions

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

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

Understanding How Ligand Functionalization Influences CO2 and N-2 Adsorption in a Sodalite Metal-Organic Framework