Michele Ceriotti

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

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EPFL STI SMX-GE
MXG 337 (Bâtiment MXG)
Station 12
CH-1015 Lausanne

41 21 693 29 39
Office: MXG 337
EPFL > STI > STI-SMX > SMX-ENS

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

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

41 21 693 29 39
41 21 693 29 25
Office: MXG 337
EPFL > STI > IMX > COSMO

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

41 21 693 29 39
EPFL > VPA > VPA-AVP-CP > SCITAS > SCITAS-CD

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Administrative data

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 in 2010. 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. His research revolves around the atomic-scale modelling of materials, based on the sampling of quantum and thermal fluctuations and on the use of machine learning to predict and rationalize structure-property relations. He has been awarded the IBM Research Forschungspreis in 2010, the Volker Heine Young Investigator Award in 2013, an ERC Starting Grant in 2016, and the IUPAP C10 Young Scientist Prize in 2018.

Publications

Infoscience publications

2021

Improving sample and feature selection with principal covariates regression

R. K. Cersonsky; B. A. Helfrecht; E. A. Engel; S. Kliavinek; M. Ceriotti

Machine Learning-Science And Technology. 2021-09-01. DOI : 10.1088/2632-2153/abfe7c.

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

G. Imbalzano; M. Ceriotti

Physical Review Materials. 2021-06-22. DOI : 10.1103/PhysRevMaterials.5.063804.

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

B. A. Helfrecht

Lausanne, EPFL, 2021. DOI : 10.5075/epfl-thesis-9032.

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

F. Giberti; G. A. Tribello; M. Ceriotti

Journal Of Chemical Theory And Computation. 2021-06-08. DOI : 10.1021/acs.jctc.0c01177.

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

A. Grisafi

Lausanne, EPFL, 2021. DOI : 10.5075/epfl-thesis-8247.

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

A. C. P. Jain; D. Marchand; A. Glensk; M. Ceriotti; W. A. Curtin

Physical Review Materials. 2021-05-26. DOI : 10.1103/PhysRevMaterials.5.053805.

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The role of feature space in atomistic learning

A. Goscinski; G. Fraux; G. Imbalzano; M. Ceriotti

Machine Learning-Science And Technology. 2021-06-01. DOI : 10.1088/2632-2153/abdaf7.

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Machine learning meets chemical physics

M. Ceriotti; C. Clementi; O. Anatole von Lilienfeld

Journal Of Chemical Physics. 2021-04-28. DOI : 10.1063/5.0051418.

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

N. Lopanitsyna; C. Ben Mahmoud; M. Ceriotti

Physical Review Materials. 2021. DOI : 10.1103/PhysRevMaterials.5.043802.

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A general and efficient framework for atomistic machine learning

F. B. C. Musil

Lausanne, EPFL, 2021. DOI : 10.5075/epfl-thesis-7997.

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Uncertainty estimation for molecular dynamics and sampling

G. Imbalzano; Y. Zhuang; V. Kapil; K. Rossi; E. A. Engel et al.

The Journal of Chemical Physics. 2021. DOI : 10.1063/5.0036522.

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Efficient implementation of atom-density representations

F. Musil; M. Veit; A. Goscinski; G. Fraux; M. J. Willatt et al.

The Journal of Chemical Physics. 2021. DOI : 10.1063/5.0044689.

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Simulating the ghost: quantum dynamics of the solvated electron

J. Lan; V. Kapil; P. Gasparotto; M. Ceriotti; M. Iannuzzi et al.

Nature Communications. 2021. DOI : 10.1038/s41467-021-20914-0.

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Multi-scale approach for the prediction of atomic scale properties

A. Grisafi; J. Nigam; M. Ceriotti

Chemical Science. 2021. DOI : 10.1039/D0SC04934D.

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Origins of structural and electronic transitions in disordered silicon

V. L. Deringer; N. Bernstein; G. Csányi; C. Ben Mahmoud; M. Ceriotti et al.

Nature. 2021-01-06. DOI : 10.1038/s41586-020-03072-z.

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2020

Structure-property maps with Kernel principal covariates regression

B. A. Helfrecht; R. K. Cersonsky; G. Fraux; M. Ceriotti

Machine Learning: Science and Technology. 2020. DOI : 10.1088/2632-2153/aba9ef.

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Learning the electronic density of states in condensed matter

C. Ben Mahmoud; A. Anelli; G. Csanyi; M. Ceriotti

Physical Review B. 2020-12-14. DOI : 10.1103/PhysRevB.102.235130.

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Characterising Structure and Stability of Materials using Machine Learning

A. Anelli

Lausanne, EPFL, 2020. DOI : 10.5075/epfl-thesis-7977.

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Incompleteness of Atomic Structure Representations

S. N. Pozdnyakov; M. J. Willatt; A. P. Bartok; C. Ortner; G. Csanyi et al.

Physical Review Letters. 2020-10-12. DOI : 10.1103/PhysRevLett.125.166001.

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Gas-sieving zeolitic membranes fabricated by condensation of precursor nanosheets

M. Dakhchoune; L. F. Villalobos; R. Semino; L. Liu; M. Rezaei et al.

Nature Materials. 2020-10-05. DOI : 10.1038/s41563-020-00822-2.

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Evidence for supercritical behaviour of high-pressure liquid hydrogen

B. Cheng; G. Mazzola; C. J. Pickard; M. Ceriotti

Nature. 2020. DOI : 10.1038/s41586-020-2677-y.

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Chemiscope: interactive structure-property explorer for materials and molecules

G. Fraux; R. Cersonsky; M. Ceriotti

Journal of Open Source Software. 2020. DOI : 10.21105/joss.02117.

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Recursive evaluation and iterative contraction of N-body equivariant features

J. Nigam; S. Pozdnyakov; M. Ceriotti

The Journal of Chemical Physics. 2020. DOI : 10.1063/5.0021116.

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

G. Csányi; M. J. Willatt; M. Ceriotti

Machine Learning Meets Quantum Physics; Springer International Publishing, 2020.

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

P. Gkeka; G. Stoltz; A. B. Farimani; Z. Belkacemi; M. Ceriotti et al.

Journal Of Chemical Theory And Computation. 2020-08-11. DOI : 10.1021/acs.jctc.0c00355.

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

K. Rossi; V. Juraskova; R. Wischert; L. Garel; C. Corminboeuf et al.

Journal Of Chemical Theory And Computation. 2020-08-11. DOI : 10.1021/acs.jctc.0c00362.

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Nuclear Quantum Effects: Fast and Accurate

V. Kapil

Lausanne, EPFL, 2020. DOI : 10.5075/epfl-thesis-7556.

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

M. Zamani; G. Imbalzano; N. Tappy; D. T. L. Alexander; S. Marti-Sanchez et al.

Advanced Materials. 2020-08-06. DOI : 10.1002/adma.202001030.

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

M. Veit; D. M. Wilkins; Y. Yang; R. A. DiStasio; M. Ceriotti

Journal Of Chemical Physics. 2020-07-14. DOI : 10.1063/5.0009106.

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Quantum kinetic energy and isotope fractionation in aqueous ionic solutions

L. Wang; M. Ceriotti; T. E. Markland

Physical Chemistry Chemical Physics. 2020-05-21. DOI : 10.1039/c9cp06483d.

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

V. Kapil; D. M. Wilkins; J. Lan; M. Ceriotti

Journal Of Chemical Physics. 2020-03-31. DOI : 10.1063/1.5141950.

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

B. Cheng; M. Ceriotti; G. A. Tribello

Journal Of Chemical Physics. 2020-01-31. DOI : 10.1063/1.5134461.

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

M. Asgari; R. Semino; P. A. Schouwink; I. Kochetygov; J. Tarver et al.

Chemistry Of Materials. 2020-02-25. DOI : 10.1021/acs.chemmater.9b04631.

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

I. Poltaysky; V. Kapil; M. Ceriotti; K. S. Kim; A. Tkatchenko

Journal Of Chemical Theory And Computation. 2020-02-01. DOI : 10.1021/acs.jctc.9b00881.

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Identifying and Tracking Defects in Dynamic Supramolecular Polymers

P. Gasparotto; D. Bochicchio; M. Ceriotti; G. M. Pavan

Journal Of Physical Chemistry B. 2020-01-23. DOI : 10.1021/acs.jpcb.9b11015.

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Iterative Unbiasing of Quasi-Equilibrium Sampling

F. Giberti; B. Cheng; G. A. Tribello; M. Ceriotti

Journal Of Chemical Theory And Computation. 2020-01-01. DOI : 10.1021/acs.jctc.9b00907.

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Atomistic modeling of the solid-liquid interface of metals and alloys

E. Baldi

Lausanne, EPFL, 2020. DOI : 10.5075/epfl-thesis-7335.

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2019

Learning (from/about) the ground-state electron density

C. Corminboeuf; M. Ceriotti; A. Fabrizio; A. Grisafi; B. Meyer et al.

2019-08-25. Fall National Meeting and Exposition of the American-Chemical-Society (ACS), San Diego, CA, Aug 25-29, 2019.

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Machine Learning at the Atomic Scale

F. Musil; M. Ceriotti

Chimia. 2019-12-01. DOI : 10.2533/chimia.2019.972.

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Determination and evaluation of the nonadditivity in wetting of molecularly heterogeneous surfaces

Z. Luo; A. Murello; D. M. Wilkins; F. Kovacik; J. Kohlbrecher et al.

Proceedings of the National Academy of Sciences. 2019-12-17. DOI : 10.1073/pnas.1916180116.

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Atomic-Scale Representation and Statistical Learning of Tensorial Properties

A. Grisafi; D. M. Wilkins; M. J. Willatt; M. Ceriotti

Machine Learning in Chemistry: Data-Driven Algorithms, Learning Systems, and Predictions; Cornell University, 2019. p. 1-21.

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Incorporating long-range physics in atomic-scale machine learning

A. Grisafi; M. Ceriotti

The Journal of Chemical Physics. 2019. DOI : 10.1063/1.5128375.

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Assessment of Approximate Methods for Anharmonic Free Energies

V. Kapil; E. A. Engel; M. Rossi; M. Ceriotti

Journal of Chemical Theory and Computation. 2019. DOI : 10.1021/acs.jctc.9b00596.

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A Bayesian approach to NMR crystal structure determination

E. A. Engel; A. Anelli; A. Hofstetter; F. M. Paruzzo; L. Emsley et al.

Physical Chemistry Chemical Physics. 2019. DOI : 10.1039/C9CP04489B.

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A new kind of atlas of zeolite building blocks

B. A. Helfrecht; R. Semino Barbaresi; G. Pireddu; S. M. Auerbach; M. Ceriotti

The Journal of Chemical Physics. 2019. DOI : 10.1063/1.5119751.

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

B. A. Helfrecht; P. Gasparotto; F. Giberti; M. Ceriotti

Frontiers in Molecular Biosciences. 2019. DOI : 10.3389/fmolb.2019.00024.

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Barely porous organic cages for hydrogen isotope separation

M. Liu; L. Zhang; M. A. Little; V. Kapil; M. Ceriotti et al.

Science. 2019. DOI : 10.1126/science.aax7427.

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Unsupervised machine learning in atomistic simulations, between predictions and understanding

M. Ceriotti

The Journal of Chemical Physics. 2019. DOI : 10.1063/1.5091842.

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An In-Situ Neutron Diffraction and DFT Study of Hydrogen Adsorption in a Sodalite-Type Metal-Organic Framework, Cu-BTTri

M. Asgari; R. Semino; P. Schouwink; I. Kochetygov; O. Trukhina et al.

European Journal of Inorganic Chemistry. 2019. DOI : 10.1002/ejic.201801253.

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Atom-density representations for machine learning

M. J. Willatt; F. Musil; M. Ceriotti

The Journal of Chemical Physics. 2019. DOI : 10.1063/1.5090481.

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Fast and Accurate Uncertainty Estimation in Chemical Machine Learning

M. J. Willatt; F. Musil; M. Ceriotti; M. A. Langovoy

Journal of Chemical Theory and Computation. 2019. DOI : 10.1021/acs.jctc.8b00959.

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Using Gaussian process regression to simulate the vibrational Raman spectra of molecular crystals

N. Raimbault; A. Grisafi; M. Ceriotti; M. Rossi

New Journal of Physics. 2019. DOI : 10.1088/1367-2630/ab4509.

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Electron density learning of non-covalent systems

A. Fabrizio; A. Grisafi; B. Meyer; M. Ceriotti; C. Corminboeuf

Chemical Science. 2019-11-07. DOI : 10.1039/c9sc02696g.

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

V. Kapil; J. Wieme; S. Vandenbrande; A. Lamaire; V. Van Speybroeck et al.

Journal of Chemical Theory and Computation. 2019. DOI : 10.1021/acs.jctc.8b01297.

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Accurate molecular polarizabilities with coupled cluster theory and machine learning

254313; A. Grisafi; Y. Yang; K. U. Lao; R. A. DiStasio et al.

Proceedings of the National Academy of Sciences. 2019. DOI : 10.1073/pnas.1816132116.

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

Y. Yang; K. U. Lao; D. M. Wilkins; A. Grisafi; M. Ceriotti et al.

Scientific Data. 2019-08-19. DOI : 10.1038/s41597-019-0157-8.

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Physics-based machine learning for materials and molecules

M. Ceriotti; E. Engel; M. Willatt

2019-03-31. National Meeting of the American-Chemical-Society (ACS), Orlando, FL, Mar 31-Apr 04, 2019.

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Energy Relaxation and Thermal Diffusion in Infrared Pump-Probe Spectroscopy of Hydrogen-Bonded Liquids

R. Dettori; M. Ceriotti; J. Hunger; L. Colombo; D. Donadio

Journal Of Physical Chemistry Letters. 2019-06-20. DOI : 10.1021/acs.jpclett.9b01272.

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i-PI 2.0: A universal force engine for advanced molecular simulations

V. Kapil; M. Rossi; O. Marsalek; R. Petraglia; Y. Litman et al.

Computer Physics Communications. 2019-03-01. DOI : 10.1016/j.cpc.2018.09.020.

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Transferable Machine-Learning Model of the Electron Density

A. Grisafi; A. Fabrizio; B. Meyer; D. M. Wilkins; C. Corminboeuf et al.

Acs Central Science. 2019-01-23. DOI : 10.1021/acscentsci.8b00551.

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Ab initio thermodynamics of liquid and solid water

B. Cheng; E. A. Engel; J. Behler; C. Dellago; M. Ceriotti

Proceedings Of The National Academy Of Sciences Of The United States Of America. 2019-01-22. DOI : 10.1073/pnas.1815117116.

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Predicting homogeneous nucleation rate from atomistic simulations

B. Cheng

Lausanne, EPFL, 2019. DOI : 10.5075/epfl-thesis-9183.

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2018

Nuclear Quantum Effects in Sodium Hydroxide Solutions from Neural Network Molecular Dynamics Simulations

M. Hellström; M. Ceriotti; J. Behler

The Journal of Physical Chemistry B. 2018. DOI : 10.1021/acs.jpcb.8b06433.

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Large-Scale Computational Screening of Molecular Organic Semiconductors Using Crystal Structure Prediction

J. Yang; S. De; J. E. Campbell; S. Li; M. Ceriotti et al.

Chemistry of Materials. 2018. DOI : 10.1021/acs.chemmater.8b01621.

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Chemical shifts in molecular solids by machine learning

F. M. Paruzzo; A. Hofstetter; F. Musil; S. De; M. Ceriotti et al.

Nature Communications. 2018. DOI : 10.1038/s41467-018-06972-x.

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Generalized convex hull construction for materials discovery

A. Anelli; E. A. Engel; C. J. Pickard; M. Ceriotti

Physical Review Materials. 2018. DOI : 10.1103/PhysRevMaterials.2.103804.

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Comparison of permutationally invariant polynomials, neural networks, and Gaussian approximation potentials in representing water interactions through many-body expansions

T. T. Nguyen; E. Székely; G. Imbalzano; J. Behler; G. Csányi et al.

The Journal of Chemical Physics. 2018. DOI : 10.1063/1.5024577.

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Decisive role of nuclear quantum effects on surface mediated water dissociation at finite temperature

Y. Litman; D. Donadio; M. Ceriotti; M. Rossi

The Journal of Chemical Physics. 2018. DOI : 10.1063/1.5002537.

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Feature optimization for atomistic machine learning yields a data-driven construction of the periodic table of the elements

M. J. Willatt; F. Musil; M. Ceriotti

Physical Chemistry Chemical Physics. 2018. DOI : 10.1039/C8CP05921G.

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

M. J. Willatt; M. Ceriotti; S. C. Althorpe

The Journal of Chemical Physics. 2018. DOI : 10.1063/1.5004808.

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Hydrogen Diffusion and Trapping in α-Iron: The Role of Quantum and Anharmonic Fluctuations

B. Cheng; A. T. Paxton; M. Ceriotti

Physical Review Letters. 2018. DOI : 10.1103/PhysRevLett.120.225901.

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Comment on "Water- water correlations in electrolyte solutions probed by hyper-Rayleigh scattering" [J. Chem. Phys. 147, 214505 (2017)]

Y. Chen; H. I. Okur; N. Dupertuis; J. Dedic; D. M. Wilkins et al.

Journal Of Chemical Physics. 2018-10-28. DOI : 10.1063/1.5023579.

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Data-driven many-body representations with chemical accuracy for molecular simulations from the gas to the condensed phase

Thuong Nguyen; E. Szekely; G. Imbalzano; J. Behler; G. Csanyi et al.

2018-08-19. 256th National Meeting and Exposition of the American-Chemical-Society (ACS) - Nanoscience, Nanotechnology and Beyond, Boston, MA, Aug 19-23, 2018.

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

B. Cheng; C. Dellago; M. Ceriotti

Physical Chemistry Chemical Physics. 2018-12-07. DOI : 10.1039/c8cp04561e.

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Applications of machine learning for studying local solvation environments

M. Groenonboom; S. De; M. Ceriotti; J. Keith

2018. 255th National Meeting and Exposition of the American-Chemical-Society (ACS) - Nexus of Food, Energy, and Water, New Orleans, LA, Mar 18-22, 2018.

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Early Stages of Precipitation In Aluminum Alloys by First-Principles and Machine-Learning Atomistic Simulations

D. Giofré

Lausanne, EPFL, 2018. DOI : 10.5075/epfl-thesis-8766.

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Communication: Computing the Tolman length for solid-liquid interfaces

B. Cheng; M. Ceriotti

The Journal of Chemical Physics. 2018. DOI : 10.1063/1.5038396.

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Mapping uncharted territory in ice from zeolite networks to ice structures

E. A. Engel; A. Anelli; M. Ceriotti; C. J. Pickard; R. J. Needs

Nature Communications. 2018. DOI : 10.1038/s41467-018-04618-6.

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Anisotropy of the Proton Momentum Distribution in Water

V. Kapil; A. Cuzzocrea; M. Ceriotti

The Journal of Physical Chemistry. 2018. DOI : 10.1021/acs.jpcb.8b03896.

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Fast-forward Langevin dynamics with momentum flips

M. Hijazi; D. M. Wilkins; M. Ceriotti

The Journal of Chemical Physics. 2018. DOI : 10.1063/1.5029833.

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Automatic selection of atomic fingerprints and reference configurations for machine-learning potentials

G. Imbalzano; A. Anelli; D. Giofré; S. Klees; J. Behler et al.

The Journal of Chemical Physics. 2018. DOI : 10.1063/1.5024611.

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An Automatic, Data-Driven Definition of Atomic-Scale Structural Motifs

P. Gasparotto

Lausanne, EPFL, 2018. DOI : 10.5075/epfl-thesis-8412.

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Analyzing Fluxional Molecules Using DORI

L. Vannay; B. Meyer; R. Petraglia; G. Sforazzini; M. Ceriotti et al.

Journal of Chemical Theory and Computation. 2018-03-24. DOI : 10.1021/acs.jctc.7b01176.

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

B. Cheng; M. Ceriotti

Physical Review B. 2018. DOI : 10.1103/PhysRevB.97.054102.

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Recognizing Local and Global Structural Motifs at the Atomic Scale

P. Gasparotto; R. H. Meißner; M. Ceriotti

Journal of Chemical Theory and Computation. 2018. DOI : 10.1021/acs.jctc.7b00993.

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Machine learning for the structure–energy–property landscapes of molecular crystals

F. Musil; S. De; J. Yang; J. E. Campbell; G. M. Day et al.

Chemical Science. 2018. DOI : 10.1039/C7SC04665K.

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Symmetry-Adapted Machine Learning for Tensorial Properties of Atomistic Systems

A. Grisafi; D. M. Wilkins; G. Csányi; M. Ceriotti

Physical Review Letters. 2018. DOI : 10.1103/PhysRevLett.120.036002.

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Nuclear quantum effects enter the mainstream

T. E. Markland; M. Ceriotti

Nature Reviews Chemistry. 2018. DOI : 10.1038/s41570-017-0109.

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Fine tuning classical and quantum molecular dynamics using a generalized Langevin equation

M. Rossi; V. Kapil; M. Ceriotti

The Journal of Chemical Physics. 2018. DOI : 10.1063/1.4990536.

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2017

Machine learning unifies the modeling of materials and molecules

A. P. Bartok; S. De; C. Poelking; N. Bernstein; J. R. Kermode et al.

Science Advances. 2017. DOI : 10.1126/sciadv.1701816.

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Extracting the interfacial free energy and anisotropy from a smooth fluctuating dividing surface

E. Baldi; M. Ceriotti; G. A. Tribello

Journal Of Physics-Condensed Matter. 2017. DOI : 10.1088/1361-648X/aa893d.

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The Gibbs free energy of homogeneous nucleation: From atomistic nuclei to the planar limit

B. Cheng; G. A. Tribello; M. Ceriotti

The Journal of Chemical Physics. 2017. DOI : 10.1063/1.4997180.

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

C. Liang; G. Tocci; D. M. Wilkins; A. Grisafi; S. Roke et al.

Physical Review B. 2017. DOI : 10.1103/PhysRevB.96.041407.

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Mapping the conformational free energy of aspartic acid in the gas phase and in aqueous solution

F. Comitani; K. Rossi; M. Ceriotti; M. E. Sanz; C. Molteni

Journal Of Chemical Physics. 2017. DOI : 10.1063/1.4979519.

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Communication: Mean-field theory of water-water correlations in electrolyte solutions

D. M. Wilkins; D. E. Manolopoulos; S. Roke; M. Ceriotti

The Journal of Chemical Physics. 2017. DOI : 10.1063/1.4983221.

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Simulating Energy Relaxation in Pump-Probe Vibrational Spectroscopy of Hydrogen-Bonded Liquids

R. Dettori; M. Ceriotti; J. Hunger; C. Melis; L. Colombo et al.

Journal Of Chemical Theory And Computation. 2017. DOI : 10.1021/acs.jctc.6b01108.

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Mapping and classifying molecules from a high-throughput structural database

S. De; F. Musil; T. Ingram; C. Baldauf; M. Ceriotti

Journal of Cheminformatics. 2017. DOI : 10.1186/s13321-017-0192-4.

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Bridging the gap between atomistic and macroscopic models of homogeneous nucleation

B. Cheng; M. Ceriotti

The Journal of Chemical Physics. 2017. DOI : 10.1063/1.4973883.

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2016

High order path integrals made easy

V. Kapil; J. Behler; M. Ceriotti

The Journal of Chemical Physics. 2016. DOI : 10.1063/1.4971438.

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

G. Tocci; C. Liang; D. M. Wilkins; S. Roke; M. Ceriotti

The Journal of Physical Chemistry Letters. 2016. DOI : 10.1021/acs.jpclett.6b01851.

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

M. Rossi; M. Ceriotti; D. E. Manolopoulos

Journal Of Physical Chemistry Letters. 2016. DOI : 10.1021/acs.jpclett.6b01093.

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Accelerated path integral methods for atomistic simulations at ultra-low temperatures

F. Uhl; D. Marx; M. Ceriotti

Journal Of Chemical Physics. 2016. DOI : 10.1063/1.4959602.

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Nuclear Quantum Effects in Water and Aqueous Systems: Experiment, Theory, and Current Challenges

M. Ceriotti; W. Fang; P. G. Kusalik; R. H. Mckenzie; A. Michaelides et al.

Chemical Reviews. 2016. DOI : 10.1021/acs.chemrev.5b00674.

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Thermally-nucleated self-assembly of water and alcohol into stable structures at hydrophobic interfaces

K. Voïtchovsky; D. Giofrè; J. José Segura; F. Stellacci; M. Ceriotti

Nature Communications. 2016. DOI : 10.1038/ncomms13064.

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Anharmonic and Quantum Fluctuations in Molecular Crystals: A First-Principles Study of the Stability of Paracetamol

M. Rossi; P. Gasparotto; M. Ceriotti

Physical Review Letters. 2016. DOI : 10.1103/PhysRevLett.117.115702.

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Vitrification: Machines learn to recognize glasses

M. Ceriotti; V. Vitelli

Nature Physics. 2016. DOI : 10.1038/nphys3757.

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Nuclear Quantum Effects in Water at the Triple Point: Using Theory as a Link Between Experiments

B. Cheng; J. Behler; M. Ceriotti

Journal Of Physical Chemistry Letters. 2016. DOI : 10.1021/acs.jpclett.6b00729.

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Probing Defects and Correlations in the Hydrogen-Bond Network of ab Initio Water

P. Gasparotto; A. A. Hassanali; M. Ceriotti

Journal of Chemical Theory and Computation. 2016. DOI : 10.1021/acs.jctc.5b01138.

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Comparing molecules and solids across structural and alchemical space

S. De; A. P. Bartók; G. Csányi; M. Ceriotti

Phys. Chem. Chem. Phys.. 2016. DOI : 10.1039/C6CP00415F.

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Electrolytes induce long-range orientational order and free energy changes in the H-bond network of bulk water

Y. Chen; H. I. Okur; N. Gomopoulos; C. Macias-Romero; P. S. Cremer et al.

Science Advances. 2016-04-01. DOI : 10.1126/sciadv.1501891.

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Accurate molecular dynamics and nuclear quantum effects at low cost by multiple steps in real and imaginary time: Using density functional theory to accelerate wavefunction methods

V. Kapil; J. Vandevondele; M. Ceriotti

The Journal of Chemical Physics. 2016. DOI : 10.1063/1.4941091.

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Beyond Static Structures: Putting Forth REMD as a Tool to Solve Problems in Computational Organic Chemistry

R. Petraglia; A. G. Nicolai; M. Wodrich; M. Ceriotti; C. Corminboeuf

Journal of Computational Chemistry. 2016. DOI : 10.1002/jcc.24025.

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2015

Solid-liquid interfacial free energy out of equilibrium

B. Cheng; G. A. Tribello; M. Ceriotti

Physical Review B. 2015. DOI : 10.1103/PhysRevB.92.180102.

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Probing the Unfolded Configurations of a β-Hairpin Using Sketch-Map

A. Ardevol; G. A. Tribello; M. Ceriotti; M. Parrinello

Journal of Chemical Theory and Computation. 2015. DOI : 10.1021/ct500950z.

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Modeling the Quantum Nature of Atomic Nuclei by Imaginary Time Path Integrals and Colored Noise

M. Ceriotti

Computational Trends in Solvation and Transport in Liquids, Lecture Notes; Schriften des Forschungszentrums Jülich, 2015. p. 1-24.

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2014

Direct path integral estimators for isotope fractionation ratios

B. Cheng; M. Ceriotti

Journal Of Chemical Physics. 2014. DOI : 10.1063/1.4904293.

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Discussion: Theoretical Horizons and Calculation

M. Ceriotti; C. Drechsel-Grau; F. Fernandez-Alonso; N. Greaves; M. Krzystyniak et al.

2014. 6th Workshop in Electron Volt Neutron Spectroscopy - Frontiers and Horizons', u'6th Workshop in Electron Volt Neutron Spectroscopy - Frontiers and Horizons']. DOI : 10.1088/1742-6596/571/1/012013.

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

F. Giberti; A. A. Hassanali; M. Ceriotti; M. Parrinello

The Journal of Physical Chemistry B. 2014. DOI : 10.1021/jp507752e.

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Ab initio simulation of particle momentum distributions in high-pressure water

M. Ceriotti

2014. 6th Workshop in Electron Volt Neutron Spectroscopy: Frontiers and Horizons, Abingdon, UK, 20–21 January 2014. p. 012011. DOI : 10.1088/1742-6596/571/1/012011.

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Communication: On the consistency of approximate quantum dynamics simulation methods for vibrational spectra in the condensed phase

M. Rossi; H. Liu; F. Paesani; J. Bowman; M. Ceriotti

The Journal of Chemical Physics. 2014. DOI : 10.1063/1.4901214.

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Recognizing molecular patterns by machine learning: An agnostic structural definition of the hydrogen bond

P. Gasparotto; M. Ceriotti

The Journal of Chemical Physics. 2014. DOI : 10.1063/1.4900655.

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Quantum fluctuations and isotope effects in ab initio descriptions of water

L. Wang; M. Ceriotti; T. E. Markland

The Journal of Chemical Physics. 2014. DOI : 10.1063/1.4894287.

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How to remove the spurious resonances from ring polymer molecular dynamics

M. Rossi; M. Ceriotti; D. E. Manolopoulos

The Journal of Chemical Physics. 2014. DOI : 10.1063/1.4883861.

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

M. Nava; M. Ceriotti; C. Dryzun; M. Parrinello

Physical Review E. 2014. DOI : 10.1103/PhysRevE.89.023302.

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i-PI: A Python interface for ab initio path integral molecular dynamics simulations

M. Ceriotti; J. More; D. E. Manolopoulos

Computer Physics Communications. 2014. DOI : 10.1016/j.cpc.2013.10.027.

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2013

Effects of High Angular Momentum on the Unimolecular Dissociation of CD2CD2OH: Theory and Comparisons with Experiment

B. G. Mckown; M. Ceriotti; C. C. Womack; E. Kamarchik; L. J. Butler et al.

Journal Of Physical Chemistry A. 2013. DOI : 10.1021/jp407913t.

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Direct Measurement of Competing Quantum Effects on the Kinetic Energy of Heavy Water upon Melting

G. Romanelli; M. Ceriotti; D. E. Manolopoulos; C. Pantalei; R. Senesi et al.

The Journal of Physical Chemistry Letters. 2013. DOI : 10.1021/jz401538r.

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Nuclear quantum effects and hydrogen bond fluctuations in water

M. Ceriotti; J. Cuny; M. Parrinello; D. E. Manolopoulos

Proceedings of the National Academy of Sciences. 2013. DOI : 10.1073/pnas.1308560110.

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A Surface-Specific Isotope Effect in Mixtures of Light and Heavy Water

J. Liu; R. S. Andino; C. M. Miller; X. Chen; D. M. Wilkins et al.

The Journal of Physical Chemistry C. 2013. DOI : 10.1021/jp311986m.

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Efficient methods and practical guidelines for simulating isotope effects

M. Ceriotti; T. E. Markland

The Journal of Chemical Physics. 2013. DOI : 10.1063/1.4772676.

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Demonstrating the Transferability and the Descriptive Power of Sketch-Map

M. Ceriotti; G. A. Tribello; M. Parrinello

Journal of Chemical Theory and Computation. 2013. DOI : 10.1021/ct3010563.

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2012

The inefficiency of re-weighted sampling and the curse of system size in high-order path integration

M. Ceriotti; G. A. R. Brain; O. Riordan; D. E. Manolopoulos

Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences. 2012. DOI : 10.1098/rspa.2011.0413.

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Using sketch-map coordinates to analyze and bias molecular dynamics simulations

G. A. Tribello; M. Ceriotti; M. Parrinello

Proceedings of the National Academy of Sciences. 2012. DOI : 10.1073/pnas.1201152109.

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Simultaneous measurement of lithium and fluorine momentum in

A. G. Seel; M. Ceriotti; P. P. Edwards; J. Mayers

Journal of Physics: Condensed Matter. 2012. DOI : 10.1088/0953-8984/24/36/365401.

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The Fuzzy Quantum Proton in the Hydrogen Chloride Hydrates

A. A. Hassanali; J. Cuny; M. Ceriotti; C. J. Pickard; M. Parrinello

Journal of the American Chemical Society. 2012. DOI : 10.1021/ja3014727.

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Efficient First-Principles Calculation of the Quantum Kinetic Energy and Momentum Distribution of Nuclei

M. Ceriotti; D. E. Manolopoulos

Physical Review Letters. 2012. DOI : 10.1103/PhysRevLett.109.100604.

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2011

Efficient multiple time scale molecular dynamics: Using colored noise thermostats to stabilize resonances

J. A. Morrone; T. E. Markland; M. Ceriotti; B. J. Berne

The Journal of Chemical Physics. 2011. DOI : 10.1063/1.3518369.

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Static disorder and structural correlations in the low-temperature phase of lithium imide

G. Miceli; M. Ceriotti; M. Bernasconi; M. Parrinello

Physical Review B. 2011. DOI : 10.1103/PhysRevB.83.054119.

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First-Principles Study of the High-Temperature Phase of Li

G. Miceli; M. Ceriotti; S. Angioletti-Uberti; M. Bernasconi; M. Parrinello

The Journal of Physical Chemistry C. 2011. DOI : 10.1021/jp200076p.

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From the Cover: Simplifying the representation of complex free-energy landscapes using sketch-map

M. Ceriotti; G. A. Tribello; M. Parrinello

Proceedings of the National Academy of Sciences. 2011. DOI : 10.1073/pnas.1108486108.

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Accelerating the convergence of path integral dynamics with a generalized Langevin equation

M. Ceriotti; D. E. Manolopoulos; M. Parrinello

The Journal of Chemical Physics. 2011. DOI : 10.1063/1.3556661.

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2010

Solid-liquid interface free energy through metadynamics simulations

S. Angioletti-Uberti; M. Ceriotti; P. D. Lee; M. W. Finnis

Physical Review B. 2010. DOI : 10.1103/PhysRevB.81.125416.

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Colored-Noise Thermostats à la Carte

M. Ceriotti; G. Bussi; M. Parrinello

Journal of Chemical Theory and Computation. 2010. DOI : 10.1021/ct900563s.

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Nuclear quantum effects in ab initio dynamics: Theory and experiments for lithium imide

M. Ceriotti; G. Miceli; A. Pietropaolo; D. Colognesi; A. Nale et al.

Physical Review B. 2010. DOI : 10.1103/PhysRevB.82.174306.

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The -thermostat: selective normal-modes excitation by colored-noise Langevin dynamics

M. Ceriotti; M. Parrinello

Procedia Computer Science. 2010. DOI : 10.1016/j.procs.2010.04.180.

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Efficient stochastic thermostatting of path integral molecular dynamics

M. Ceriotti; M. Parrinello; T. E. Markland; D. E. Manolopoulos

The Journal of Chemical Physics. 2010. DOI : 10.1063/1.3489925.

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A self-learning algorithm for biased molecular dynamics

G. A. Tribello; M. Ceriotti; M. Parrinello

Proceedings of the National Academy of Sciences. 2010. DOI : 10.1073/pnas.1011511107.

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2009

Nuclear Quantum Effects in Solids Using a Colored-Noise Thermostat

M. Ceriotti; G. Bussi; M. Parrinello

Physical Review Letters. 2009. DOI : 10.1103/PhysRevLett.103.030603.

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Langevin Equation with Colored Noise for Constant-Temperature Molecular Dynamics Simulations

M. Ceriotti; G. Bussi; M. Parrinello

Physical Review Letters. 2009. DOI : 10.1103/PhysRevLett.102.020601.

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Ab initio study of the diffusion and decomposition pathways of SiHx species on Si(100)

M. Ceriotti; S. Cereda; F. Montalenti; L. Miglio; M. Bernasconi

Physical Review B. 2009. DOI : 10.1103/PhysRevB.79.165437.

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2008

An efficient and accurate decomposition of the Fermi operator

M. Ceriotti; T. D. KüHne; M. Parrinello

The Journal of Chemical Physics. 2008. DOI : 10.1063/1.2949515.

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First principles study of Ge∕Si exchange mechanisms at the Si(001) surface

F. Zipoli; S. Cereda; M. Ceriotti; M. Bernasconi; L. Miglio et al.

Applied Physics Letters. 2008. DOI : 10.1063/1.2926683.

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2007

Quantitative estimate of H abstraction by thermal SiH3 on hydrogenated Si(001)(2×1)

S. Cereda; M. Ceriotti; F. Montalenti; M. Bernasconi; L. Miglio

Physical Review B. 2007. DOI : 10.1103/PhysRevB.75.235311.

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Diffusion and desorption of SiH3 on hydrogenated H:Si(100)-(2×1) from first principles

M. Ceriotti; M. Bernasconi

Physical Review B. 2007. DOI : 10.1103/PhysRevB.76.245309.

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Conjugate gradient heat bath for ill-conditioned actions

M. Ceriotti; G. Bussi; M. Parrinello

Physical Review E. 2007. DOI : 10.1103/PhysRevE.76.026707.

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2006

Impact-driven effects in thin-film growth: steering and transient mobility at the Ag(110) surface

M. Ceriotti; R. Ferrando; F. Montalenti

Nanotechnology. 2006. DOI : 10.1088/0957-4484/17/14/033.

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Ab initio study of the vibrational properties of crystalline TeO2: The α, β, and γ phases

M. Ceriotti; F. Pietrucci; M. Bernasconi

Physical Review B. 2006. DOI : 10.1103/PhysRevB.73.104304.

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Teaching & PhD

Teaching

Materials Science and Engineering

PhD Programs

Doctoral Program in Materials Science and Engineering

Doctoral Program in Physics

PhD Students

Ben Mahmoud Chiheb, Goscinski Alexander Jan, Huguenin-Dumittan Kevin Kazuki, Imbalzano Giulio, Lopanitsyna Nataliya, Maksimov Dmitrii, Nigam Jigyasa, Pozdnyakov Sergey,

Past EPFL PhD Students

Anelli Andrea , Baldi Edoardo , Cheng Bingqing , Gasparotto Piero , Giofré Daniele , Grisafi Andrea , Helfrecht Benjamin Aaron , Kapil Venkat , Musil Félix Benedito Clément ,

Courses

Statistical mechanics

This course presents an introduction to statistical mechanics geared towards materials scientists. The concepts of macroscopic thermodynamics will be related to a microscopic picture and a statistical interpretation. Lectures and exercises will be complemented with hands-on simulation projects.

Statistical methods in atomistic computer simulations

The course gives an overview of atomistic simulation methods, combining theoretical lectures and hands-on sessions. It covers the basics (molecular dynamics and monte carlo sampling) and also more advanced topics (accelerated sampling of rare events, and non-linear dimensionality reduction).

CCMX Winter School - Additive Manufacturing of Metals and the Material Science Behind It

This course is designed to cover a series of important scientific aspects related to the field of additive manufacturing of metals and alloys and to provide an in-depth review of corresponding fundamentals. It features 9 modules consisting of presentations given by lecturers and the participants.

Lecture series on scientific machine learning

This lecture presents ongoing work on how scientific questions can be tackled using machine learning. Machine learning enables extracting knowledge from data computationally and in an automatized way. We will learn on examples how this is influencing the very scientific method.