Michele Ceriotti
+41 21 693 29 39
EPFL
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EPFL STI IMX COSMO
MXG 337 (Bâtiment MXG)
Station 12
1015 Lausanne
Web site: Web site: https://cosmo.epfl.ch/
+41 21 693 29 39
EPFL
>
STI
>
IMX
>
IMX-GE
+41 21 693 29 39
EPFL
>
STI
>
STI-DEC
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STI-DIR
Fields of expertise
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
2025
PLUMED Tutorials: A collaborative, community-driven learning ecosystem
JOURNAL OF CHEMICAL PHYSICS. 2025. DOI : 10.1063/5.0251501.Advancing understanding and practical performance of machine learning interatomic potentials
Lausanne, EPFL, 2025. DOI : 10.5075/epfl-thesis-10703.Adaptive energy reference for machine-learning models of the electronic density of states
Physical Review Materials. 2025. DOI : 10.1103/PhysRevMaterials.9.013802.2024
A prediction rigidity formalism for low-cost uncertainties in trained neural networks
Machine Learning: Science and Technology. 2024. DOI : 10.1088/2632-2153/ad805f.Probing the effects of broken symmetries in machine learning
Machine Learning: Science and Technology. 2024. DOI : 10.1088/2632-2153/ad86a0.Uncertainty quantification by direct propagation of shallow ensembles
Machine Learning: Science and Technology. 2024. DOI : 10.1088/2632-2153/ad594a.Prediction rigidities for data-driven chemistry
Faraday Discussions. 2024. DOI : 10.1039/d4fd00101j.i-PI 3.0: A flexible and efficient framework for advanced atomistic simulations
The Journal of chemical physics. 2024. DOI : 10.1063/5.0215869.Integrating symmetry and physical constraints into atomic-scale machine learning
Lausanne, EPFL, 2024. DOI : 10.5075/epfl-thesis-10848.Wigner kernels: Body-ordered equivariant machine learning without a basis
JOURNAL OF CHEMICAL PHYSICS. 2024. DOI : 10.1063/5.0208746.Modeling the ferroelectric phase transition in barium titanate with DFT accuracy and converged sampling
Physical Review B. 2024. DOI : 10.1103/PhysRevB.110.024101.Thermal conductivity of Li 3 PS 4 solid electrolytes with ab initio accuracy
Physical Review Materials. 2024. DOI : 10.1103/PhysRevMaterials.8.065403.Surface segregation in high-entropy alloys from alchemical machine learning
Journal Of Physics-Materials. 2024. DOI : 10.1088/2515-7639/ad2983.Electronic Excited States from Physically Constrained Machine Learning
ACS Central Science. 2024. DOI : 10.1021/acscentsci.3c01480.Mechanism of Charge Transport in Lithium Thiophosphate
Chemistry of Materials. 2024. DOI : 10.1021/acs.chemmater.3c02726.Efficient and insightful descriptors for representing molecular and material space
Lausanne, EPFL, 2024. DOI : 10.5075/epfl-thesis-10323.2023
Accelerated chemical science with AI
Digital Discovery. 2023. DOI : 10.1039/d3dd00213f.Robustness of Local Predictions in Atomistic Machine Learning Models
Journal of Chemical Theory and Computation. 2023. DOI : 10.1021/acs.jctc.3c00704.Physics-Inspired Equivariant Descriptors of Nonbonded Interactions
The Journal of Physical Chemistry Letters. 2023. DOI : 10.1021/acs.jpclett.3c02375.Fast evaluation of spherical harmonics with sphericart
Journal Of Chemical Physics. 2023. DOI : 10.1063/5.0156307.Teaching & PhD
Teaching
Materials Science and Engineering