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/
EPFL SCITAS-CD
PH A2 364 (Bâtiment PH)
Station 3
1015 Lausanne
+41 21 693 29 39
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PH A2 364
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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
Thermal conductivity of Li 3 PS 4 solid electrolytes with ab initio accuracy
Physical Review Materials. 2024-06-12. DOI : 10.1103/PhysRevMaterials.8.065403.Surface segregation in high-entropy alloys from alchemical machine learning
Journal Of Physics-Materials. 2024-04-01. DOI : 10.1088/2515-7639/ad2983.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. 2023-12-06. DOI : 10.1039/d3dd00213f.Natural aging and vacancy trapping in Al-6xxx
Journal Of Materials Research. 2023-12-21. DOI : 10.1557/s43578-023-01245-w.Physics-Inspired Equivariant Descriptors of Nonbonded Interactions
Journal Of Physical Chemistry Letters. 2023-10-20. DOI : 10.1021/acs.jpclett.3c02375.Robustness of Local Predictions in Atomistic Machine Learning Models
Journal Of Chemical Theory And Computation. 2023-11-10. DOI : 10.1021/acs.jctc.3c00704.Fast evaluation of spherical harmonics with sphericart
Journal Of Chemical Physics. 2023-08-14. DOI : 10.1063/5.0156307.Modelling of metal alloys in realistic conditions with machine learning
Lausanne, EPFL, 2023. DOI : 10.5075/epfl-thesis-9710.Modeling high-entropy transition metal alloys with alchemical compression
Physical Review Materials. 2023-04-26. DOI : 10.1103/PhysRevMaterials.7.045802.Machine-learning the electronic density of states: electronic properties without quantum mechanics
Lausanne, EPFL, 2023. DOI : 10.5075/epfl-thesis-10071.A data-driven interpretation of the stability of organic molecular crystals
Chemical Science. 2023-01-16. DOI : 10.1039/d2sc06198h.2022
Ranking the synthesizability of hypothetical zeolites with the sorting hat
Digital Discovery. 2022. DOI : 10.1039/D2DD00056C.Roadmap on Machine learning in electronic structure
Electronic Structure. 2022-06-01. DOI : 10.1088/2516-1075/ac572f.A smooth basis for atomistic machine learning
Journal Of Chemical Physics. 2022-12-21. DOI : 10.1063/5.0124363.Beyond potentials: Integrated machine learning models for materials
Mrs Bulletin. 2022-12-06. DOI : 10.1557/s43577-022-00440-0.Incompleteness of graph neural networks for points clouds in three dimensions
Machine Learning-Science And Technology. 2022-12-01. DOI : 10.1088/2632-2153/aca1f8.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)]
Journal Of Chemical Physics. 2022-11-07. DOI : 10.1063/5.0088404.Predicting hot-electron free energies from ground-state data
Physical Review B. 2022-09-27. DOI : 10.1103/PhysRevB.106.L121116.Thermodynamics and dielectric response of BaTiO3 by data-driven modeling
Npj Computational Materials. 2022-09-29. DOI : 10.1038/s41524-022-00845-0.A Machine Learning Model of Chemical Shifts for Chemically and Structurally Diverse Molecular Solids
Journal Of Physical Chemistry C. 2022-09-22. DOI : 10.1021/acs.jpcc.2c03854.Characterization and prediction of peptide structures on inorganic surfaces
Lausanne, EPFL, 2022. DOI : 10.5075/epfl-thesis-9740.Unified theory of atom-centered representations and message-passing machine-learning schemes
Journal Of Chemical Physics. 2022-05-28. DOI : 10.1063/5.0087042.Local Kernel Regression and Neural Network Approaches to the Conformational Landscapes of Oligopeptides
Journal of Chemical Theory and Computation. 2022. DOI : 10.1021/acs.jctc.1c00813.Equivariant representations for molecular Hamiltonians and N-center atomic-scale properties
Journal Of Chemical Physics. 2022-01-07. DOI : 10.1063/5.0072784.2021
Local invertibility and sensitivity of atomic structure-feature mappings
Open Research Europe. 2021. DOI : 10.12688/openreseurope.14156.1.2020 JCP Emerging Investigator Special Collection
Journal Of Chemical Physics. 2021-12-21. DOI : 10.1063/5.0078934.Reply to: On the liquid-liquid phase transition of dense hydrogen
Nature. 2021-12-16. DOI : 10.1038/s41586-021-04079-w.Learning Electron Densities in the Condensed Phase
Journal Of Chemical Theory And Computation. 2021-11-09. DOI : 10.1021/acs.jctc.1c00576.Bayesian probabilistic assignment of chemical shifts in organic solids
Science Advances. 2021-11-01. DOI : 10.1126/sciadv.abk2341.Transferable machine-learning models of complex materials: the case of GaAs
Lausanne, EPFL, 2021. DOI : 10.5075/epfl-thesis-8457.Optimal radial basis for density-based atomic representations
Journal Of Chemical Physics. 2021-09-14. DOI : 10.1063/5.0057229.Introduction: Machine Learning at the Atomic Scale
Chemical Reviews. 2021-08-25. DOI : 10.1021/acs.chemrev.1c00598.Gaussian Process Regression for Materials and Molecules
Chemical Reviews. 2021-08-25. DOI : 10.1021/acs.chemrev.1c00022.Chemical physics software
Journal Of Chemical Physics. 2021-07-07. DOI : 10.1063/5.0059886.Physics-Inspired Structural Representations for Molecules and Materials
Chemical Reviews. 2021-08-25. DOI : 10.1021/acs.chemrev.1c00021.Importance of Nuclear Quantum Effects for NMR Crystallography
Journal Of Physical Chemistry Letters. 2021-08-19. DOI : 10.1021/acs.jpclett.1c01987.Quantum vibronic effects on the electronic properties of solid and molecular carbon
Physical Review Materials. 2021-07-26. DOI : 10.1103/PhysRevMaterials.5.L070801.Improving sample and feature selection with principal covariates regression
Machine Learning-Science And Technology. 2021-09-01. DOI : 10.1088/2632-2153/abfe7c.Modeling the Ga/As binary system across temperatures and compositions from first principles
Physical Review Materials. 2021-06-22. DOI : 10.1103/PhysRevMaterials.5.063804.Structure-Property Relationships in Complex Materials by Combining Supervised and Unsupervised Machine Learning
Lausanne, EPFL, 2021. DOI : 10.5075/epfl-thesis-9032.Global Free-Energy Landscapes as a Smoothly Joined Collection of Local Maps
Journal Of Chemical Theory And Computation. 2021-06-08. DOI : 10.1021/acs.jctc.0c01177.Physics-enhanced machine learning with symmetry-adapted and long-range representations
Lausanne, EPFL, 2021. DOI : 10.5075/epfl-thesis-8247.Machine learning for metallurgy III: A neural network potential for Al-Mg-Si
Physical Review Materials. 2021-05-26. DOI : 10.1103/PhysRevMaterials.5.053805.The role of feature space in atomistic learning
Machine Learning-Science And Technology. 2021-06-01. DOI : 10.1088/2632-2153/abdaf7.Machine learning meets chemical physics
Journal Of Chemical Physics. 2021-04-28. DOI : 10.1063/5.0051418.Finite-temperature materials modeling from the quantum nuclei to the hot electron regime
Physical Review Materials. 2021. DOI : 10.1103/PhysRevMaterials.5.043802.A general and efficient framework for atomistic machine learning
Lausanne, EPFL, 2021. DOI : 10.5075/epfl-thesis-7997.Uncertainty estimation for molecular dynamics and sampling
The Journal of Chemical Physics. 2021. DOI : 10.1063/5.0036522.Efficient implementation of atom-density representations
The Journal of Chemical Physics. 2021. DOI : 10.1063/5.0044689.Simulating the ghost: quantum dynamics of the solvated electron
Nature Communications. 2021. DOI : 10.1038/s41467-021-20914-0.Multi-scale approach for the prediction of atomic scale properties
Chemical Science. 2021. DOI : 10.1039/D0SC04934D.Origins of structural and electronic transitions in disordered silicon
Nature. 2021-01-06. DOI : 10.1038/s41586-020-03072-z.2020
Structure-property maps with Kernel principal covariates regression
Machine Learning: Science and Technology. 2020. DOI : 10.1088/2632-2153/aba9ef.Learning the electronic density of states in condensed matter
Physical Review B. 2020-12-14. DOI : 10.1103/PhysRevB.102.235130.Characterising Structure and Stability of Materials using Machine Learning
Lausanne, EPFL, 2020. DOI : 10.5075/epfl-thesis-7977.Incompleteness of Atomic Structure Representations
Physical Review Letters. 2020-10-12. DOI : 10.1103/PhysRevLett.125.166001.Gas-sieving zeolitic membranes fabricated by condensation of precursor nanosheets
Nature Materials. 2020-10-05. DOI : 10.1038/s41563-020-00822-2.Evidence for supercritical behaviour of high-pressure liquid hydrogen
Nature. 2020. DOI : 10.1038/s41586-020-2677-y.Chemiscope: interactive structure-property explorer for materials and molecules
Journal of Open Source Software. 2020. DOI : 10.21105/joss.02117.Recursive evaluation and iterative contraction of N-body equivariant features
The Journal of Chemical Physics. 2020. DOI : 10.1063/5.0021116.Machine-Learning of Atomic-Scale Properties Based on Physical Principles
Machine Learning Meets Quantum Physics; Springer International Publishing, 2020.Machine Learning Force Fields and Coarse-Grained Variables in Molecular Dynamics: Application to Materials and Biological Systems
Journal Of Chemical Theory And Computation. 2020-08-11. DOI : 10.1021/acs.jctc.0c00355.Simulating Solvation and Acidity in Complex Mixtures with First-Principles Accuracy: The Case of CH3SO3H and H2O2 in Phenol
Journal Of Chemical Theory And Computation. 2020-08-11. DOI : 10.1021/acs.jctc.0c00362.Nuclear Quantum Effects: Fast and Accurate
Lausanne, EPFL, 2020. DOI : 10.5075/epfl-thesis-7556.3D Ordering at the Liquid-Solid Polar Interface of Nanowires
Advanced Materials. 2020-08-06. DOI : 10.1002/adma.202001030.Predicting molecular dipole moments by combining atomic partial charges and atomic dipoles
Journal Of Chemical Physics. 2020-07-14. DOI : 10.1063/5.0009106.Quantum kinetic energy and isotope fractionation in aqueous ionic solutions
Physical Chemistry Chemical Physics. 2020-05-21. DOI : 10.1039/c9cp06483d.Inexpensive modeling of quantum dynamics using path integral generalized Langevin equation thermostats
Journal Of Chemical Physics. 2020-03-31. DOI : 10.1063/1.5141950.Classical nucleation theory predicts the shape of the nucleus in homogeneous solidification
Journal Of Chemical Physics. 2020-01-31. DOI : 10.1063/1.5134461.Understanding How Ligand Functionalization Influences CO2 and N-2 Adsorption in a Sodalite Metal-Organic Framework
Chemistry Of Materials. 2020-02-25. DOI : 10.1021/acs.chemmater.9b04631.Accurate Description of Nuclear Quantum Effects with High-Order Perturbed Path Integrals (HOPPI)
Journal Of Chemical Theory And Computation. 2020-02-01. DOI : 10.1021/acs.jctc.9b00881.Identifying and Tracking Defects in Dynamic Supramolecular Polymers
Journal Of Physical Chemistry B. 2020-01-23. DOI : 10.1021/acs.jpcb.9b11015.Iterative Unbiasing of Quasi-Equilibrium Sampling
Journal Of Chemical Theory And Computation. 2020-01-01. DOI : 10.1021/acs.jctc.9b00907.Atomistic modeling of the solid-liquid interface of metals and alloys
Lausanne, EPFL, 2020. DOI : 10.5075/epfl-thesis-7335.2019
Learning (from/about) the ground-state electron density
2019-08-25. Fall National Meeting and Exposition of the American-Chemical-Society (ACS), San Diego, CA, Aug 25-29, 2019.Machine Learning at the Atomic Scale
Chimia. 2019-12-01. DOI : 10.2533/chimia.2019.972.Determination and evaluation of the nonadditivity in wetting of molecularly heterogeneous surfaces
Proceedings of the National Academy of Sciences. 2019-12-17. DOI : 10.1073/pnas.1916180116.Atomic-Scale Representation and Statistical Learning of Tensorial Properties
Machine Learning in Chemistry: Data-Driven Algorithms, Learning Systems, and Predictions; Cornell University, 2019. p. 1-21.Incorporating long-range physics in atomic-scale machine learning
The Journal of Chemical Physics. 2019. DOI : 10.1063/1.5128375.Assessment of Approximate Methods for Anharmonic Free Energies
Journal of Chemical Theory and Computation. 2019. DOI : 10.1021/acs.jctc.9b00596.A Bayesian approach to NMR crystal structure determination
Physical Chemistry Chemical Physics. 2019. DOI : 10.1039/C9CP04489B.A new kind of atlas of zeolite building blocks
The Journal of Chemical Physics. 2019. DOI : 10.1063/1.5119751.Atomic Motif Recognition in (Bio)Polymers: Benchmarks From the Protein Data Bank
Frontiers in Molecular Biosciences. 2019. DOI : 10.3389/fmolb.2019.00024.Barely porous organic cages for hydrogen isotope separation
Science. 2019. DOI : 10.1126/science.aax7427.Unsupervised machine learning in atomistic simulations, between predictions and understanding
The Journal of Chemical Physics. 2019. DOI : 10.1063/1.5091842.An In-Situ Neutron Diffraction and DFT Study of Hydrogen Adsorption in a Sodalite-Type Metal-Organic Framework, Cu-BTTri
European Journal of Inorganic Chemistry. 2019. DOI : 10.1002/ejic.201801253.Atom-density representations for machine learning
The Journal of Chemical Physics. 2019. DOI : 10.1063/1.5090481.Fast and Accurate Uncertainty Estimation in Chemical Machine Learning
Journal of Chemical Theory and Computation. 2019. DOI : 10.1021/acs.jctc.8b00959.Using Gaussian process regression to simulate the vibrational Raman spectra of molecular crystals
New Journal of Physics. 2019. DOI : 10.1088/1367-2630/ab4509.Electron density learning of non-covalent systems
Chemical Science. 2019-11-07. DOI : 10.1039/c9sc02696g.Modeling the Structural and Thermal Properties of Loaded Metal–Organic Frameworks. An Interplay of Quantum and Anharmonic Fluctuations
Journal of Chemical Theory and Computation. 2019. DOI : 10.1021/acs.jctc.8b01297.Accurate molecular polarizabilities with coupled cluster theory and machine learning
Proceedings of the National Academy of Sciences. 2019. DOI : 10.1073/pnas.1816132116.Quantum mechanical static dipole polarizabilities in the QM7b and AlphaML showcase databases
Scientific Data. 2019-08-19. DOI : 10.1038/s41597-019-0157-8.Physics-based machine learning for materials and molecules
2019-03-31. National Meeting of the American-Chemical-Society (ACS), Orlando, FL, Mar 31-Apr 04, 2019.Energy Relaxation and Thermal Diffusion in Infrared Pump-Probe Spectroscopy of Hydrogen-Bonded Liquids
Journal Of Physical Chemistry Letters. 2019-06-20. DOI : 10.1021/acs.jpclett.9b01272.i-PI 2.0: A universal force engine for advanced molecular simulations
Computer Physics Communications. 2019-03-01. DOI : 10.1016/j.cpc.2018.09.020.Transferable Machine-Learning Model of the Electron Density
Acs Central Science. 2019-01-23. DOI : 10.1021/acscentsci.8b00551.Ab initio thermodynamics of liquid and solid water
Proceedings Of The National Academy Of Sciences Of The United States Of America. 2019-01-22. DOI : 10.1073/pnas.1815117116.Predicting homogeneous nucleation rate from atomistic simulations
Lausanne, EPFL, 2019. DOI : 10.5075/epfl-thesis-9183.2018
Nuclear Quantum Effects in Sodium Hydroxide Solutions from Neural Network Molecular Dynamics Simulations
The Journal of Physical Chemistry B. 2018. DOI : 10.1021/acs.jpcb.8b06433.Large-Scale Computational Screening of Molecular Organic Semiconductors Using Crystal Structure Prediction
Chemistry of Materials. 2018. DOI : 10.1021/acs.chemmater.8b01621.Chemical shifts in molecular solids by machine learning
Nature Communications. 2018. DOI : 10.1038/s41467-018-06972-x.Generalized convex hull construction for materials discovery
Physical Review Materials. 2018. DOI : 10.1103/PhysRevMaterials.2.103804.Comparison of permutationally invariant polynomials, neural networks, and Gaussian approximation potentials in representing water interactions through many-body expansions
The Journal of Chemical Physics. 2018. DOI : 10.1063/1.5024577.Decisive role of nuclear quantum effects on surface mediated water dissociation at finite temperature
The Journal of Chemical Physics. 2018. DOI : 10.1063/1.5002537.Feature optimization for atomistic machine learning yields a data-driven construction of the periodic table of the elements
Physical Chemistry Chemical Physics. 2018. DOI : 10.1039/C8CP05921G.Approximating Matsubara dynamics using the planetary model: Tests on liquid water and ice
The Journal of Chemical Physics. 2018. DOI : 10.1063/1.5004808.Hydrogen Diffusion and Trapping in α-Iron: The Role of Quantum and Anharmonic Fluctuations
Physical Review Letters. 2018. DOI : 10.1103/PhysRevLett.120.225901.Comment on "Water- water correlations in electrolyte solutions probed by hyper-Rayleigh scattering" [J. Chem. Phys. 147, 214505 (2017)]
Journal Of Chemical Physics. 2018-10-28. DOI : 10.1063/1.5023579.Data-driven many-body representations with chemical accuracy for molecular simulations from the gas to the condensed phase
2018-08-19. 256th National Meeting and Exposition of the American-Chemical-Society (ACS) - Nanoscience, Nanotechnology and Beyond, Boston, MA, Aug 19-23, 2018.Theoretical prediction of the homogeneous ice nucleation rate: disentangling thermodynamics and kinetics
Physical Chemistry Chemical Physics. 2018-12-07. DOI : 10.1039/c8cp04561e.Applications of machine learning for studying local solvation environments
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.Early Stages of Precipitation In Aluminum Alloys by First-Principles and Machine-Learning Atomistic Simulations
Lausanne, EPFL, 2018. DOI : 10.5075/epfl-thesis-8766.Communication: Computing the Tolman length for solid-liquid interfaces
The Journal of Chemical Physics. 2018. DOI : 10.1063/1.5038396.Mapping uncharted territory in ice from zeolite networks to ice structures
Nature Communications. 2018. DOI : 10.1038/s41467-018-04618-6.Anisotropy of the Proton Momentum Distribution in Water
The Journal of Physical Chemistry. 2018. DOI : 10.1021/acs.jpcb.8b03896.Fast-forward Langevin dynamics with momentum flips
The Journal of Chemical Physics. 2018. DOI : 10.1063/1.5029833.Automatic selection of atomic fingerprints and reference configurations for machine-learning potentials
The Journal of Chemical Physics. 2018. DOI : 10.1063/1.5024611.An Automatic, Data-Driven Definition of Atomic-Scale Structural Motifs
Lausanne, EPFL, 2018. DOI : 10.5075/epfl-thesis-8412.Analyzing Fluxional Molecules Using DORI
Journal of Chemical Theory and Computation. 2018-03-24. DOI : 10.1021/acs.jctc.7b01176.Computing the absolute Gibbs free energy in atomistic simulations: Applications to defects in solids
Physical Review B. 2018. DOI : 10.1103/PhysRevB.97.054102.Recognizing Local and Global Structural Motifs at the Atomic Scale
Journal of Chemical Theory and Computation. 2018. DOI : 10.1021/acs.jctc.7b00993.Machine learning for the structure–energy–property landscapes of molecular crystals
Chemical Science. 2018. DOI : 10.1039/C7SC04665K.Symmetry-Adapted Machine Learning for Tensorial Properties of Atomistic Systems
Physical Review Letters. 2018. DOI : 10.1103/PhysRevLett.120.036002.Nuclear quantum effects enter the mainstream
Nature Reviews Chemistry. 2018. DOI : 10.1038/s41570-017-0109.Fine tuning classical and quantum molecular dynamics using a generalized Langevin equation
The Journal of Chemical Physics. 2018. DOI : 10.1063/1.4990536.2017
Machine learning unifies the modeling of materials and molecules
Science Advances. 2017. DOI : 10.1126/sciadv.1701816.Extracting the interfacial free energy and anisotropy from a smooth fluctuating dividing surface
Journal Of Physics-Condensed Matter. 2017. DOI : 10.1088/1361-648X/aa893d.The Gibbs free energy of homogeneous nucleation: From atomistic nuclei to the planar limit
The Journal of Chemical Physics. 2017. DOI : 10.1063/1.4997180.Solvent fluctuations and nuclear quantum effects modulate the molecular hyperpolarizability of water
Physical Review B. 2017. DOI : 10.1103/PhysRevB.96.041407.Mapping the conformational free energy of aspartic acid in the gas phase and in aqueous solution
Journal Of Chemical Physics. 2017. DOI : 10.1063/1.4979519.Communication: Mean-field theory of water-water correlations in electrolyte solutions
The Journal of Chemical Physics. 2017. DOI : 10.1063/1.4983221.Simulating Energy Relaxation in Pump-Probe Vibrational Spectroscopy of Hydrogen-Bonded Liquids
Journal Of Chemical Theory And Computation. 2017. DOI : 10.1021/acs.jctc.6b01108.Mapping and classifying molecules from a high-throughput structural database
Journal of Cheminformatics. 2017. DOI : 10.1186/s13321-017-0192-4.Bridging the gap between atomistic and macroscopic models of homogeneous nucleation
The Journal of Chemical Physics. 2017. DOI : 10.1063/1.4973883.2016
High order path integrals made easy
The Journal of Chemical Physics. 2016. DOI : 10.1063/1.4971438.Second-Harmonic Scattering as a Probe of Structural Correlations in Liquids
The Journal of Physical Chemistry Letters. 2016. DOI : 10.1021/acs.jpclett.6b01851.Nuclear Quantum Effects in H+ and OH- Diffusion along Confined Water Wires
Journal Of Physical Chemistry Letters. 2016. DOI : 10.1021/acs.jpclett.6b01093.Accelerated path integral methods for atomistic simulations at ultra-low temperatures
Journal Of Chemical Physics. 2016. DOI : 10.1063/1.4959602.Nuclear Quantum Effects in Water and Aqueous Systems: Experiment, Theory, and Current Challenges
Chemical Reviews. 2016. DOI : 10.1021/acs.chemrev.5b00674.Thermally-nucleated self-assembly of water and alcohol into stable structures at hydrophobic interfaces
Nature Communications. 2016. DOI : 10.1038/ncomms13064.Anharmonic and Quantum Fluctuations in Molecular Crystals: A First-Principles Study of the Stability of Paracetamol
Physical Review Letters. 2016. DOI : 10.1103/PhysRevLett.117.115702.Vitrification: Machines learn to recognize glasses
Nature Physics. 2016. DOI : 10.1038/nphys3757.Nuclear Quantum Effects in Water at the Triple Point: Using Theory as a Link Between Experiments
Journal Of Physical Chemistry Letters. 2016. DOI : 10.1021/acs.jpclett.6b00729.Probing Defects and Correlations in the Hydrogen-Bond Network of ab Initio Water
Journal of Chemical Theory and Computation. 2016. DOI : 10.1021/acs.jctc.5b01138.Comparing molecules and solids across structural and alchemical space
Phys. Chem. Chem. Phys.. 2016. DOI : 10.1039/C6CP00415F.Electrolytes induce long-range orientational order and free energy changes in the H-bond network of bulk water
Science Advances. 2016-04-01. DOI : 10.1126/sciadv.1501891.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
The Journal of Chemical Physics. 2016. DOI : 10.1063/1.4941091.Beyond Static Structures: Putting Forth REMD as a Tool to Solve Problems in Computational Organic Chemistry
Journal of Computational Chemistry. 2016. DOI : 10.1002/jcc.24025.2015
Solid-liquid interfacial free energy out of equilibrium
Physical Review B. 2015. DOI : 10.1103/PhysRevB.92.180102.Probing the Unfolded Configurations of a β-Hairpin Using Sketch-Map
Journal of Chemical Theory and Computation. 2015. DOI : 10.1021/ct500950z.Modeling the Quantum Nature of Atomic Nuclei by Imaginary Time Path Integrals and Colored Noise
Computational Trends in Solvation and Transport in Liquids, Lecture Notes; Schriften des Forschungszentrums Jülich, 2015. p. 1-24.2014
Direct path integral estimators for isotope fractionation ratios
Journal Of Chemical Physics. 2014. DOI : 10.1063/1.4904293.Discussion: Theoretical Horizons and Calculation
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.The Role of Quantum Effects on Structural and Electronic Fluctuations in Neat and Charged Water
The Journal of Physical Chemistry B. 2014. DOI : 10.1021/jp507752e.Ab initio simulation of particle momentum distributions in high-pressure water
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.Communication: On the consistency of approximate quantum dynamics simulation methods for vibrational spectra in the condensed phase
The Journal of Chemical Physics. 2014. DOI : 10.1063/1.4901214.Recognizing molecular patterns by machine learning: An agnostic structural definition of the hydrogen bond
The Journal of Chemical Physics. 2014. DOI : 10.1063/1.4900655.Quantum fluctuations and isotope effects in ab initio descriptions of water
The Journal of Chemical Physics. 2014. DOI : 10.1063/1.4894287.How to remove the spurious resonances from ring polymer molecular dynamics
The Journal of Chemical Physics. 2014. DOI : 10.1063/1.4883861.Evaluating functions of positive-definite matrices using colored-noise thermostats
Physical Review E. 2014. DOI : 10.1103/PhysRevE.89.023302.i-PI: A Python interface for ab initio path integral molecular dynamics simulations
Computer Physics Communications. 2014. DOI : 10.1016/j.cpc.2013.10.027.2013
Effects of High Angular Momentum on the Unimolecular Dissociation of CD2CD2OH: Theory and Comparisons with Experiment
Journal Of Physical Chemistry A. 2013. DOI : 10.1021/jp407913t.Direct Measurement of Competing Quantum Effects on the Kinetic Energy of Heavy Water upon Melting
The Journal of Physical Chemistry Letters. 2013. DOI : 10.1021/jz401538r.Nuclear quantum effects and hydrogen bond fluctuations in water
Proceedings of the National Academy of Sciences. 2013. DOI : 10.1073/pnas.1308560110.A Surface-Specific Isotope Effect in Mixtures of Light and Heavy Water
The Journal of Physical Chemistry C. 2013. DOI : 10.1021/jp311986m.Efficient methods and practical guidelines for simulating isotope effects
The Journal of Chemical Physics. 2013. DOI : 10.1063/1.4772676.Demonstrating the Transferability and the Descriptive Power of Sketch-Map
Journal of Chemical Theory and Computation. 2013. DOI : 10.1021/ct3010563.2012
The inefficiency of re-weighted sampling and the curse of system size in high-order path integration
Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences. 2012. DOI : 10.1098/rspa.2011.0413.Using sketch-map coordinates to analyze and bias molecular dynamics simulations
Proceedings of the National Academy of Sciences. 2012. DOI : 10.1073/pnas.1201152109.Simultaneous measurement of lithium and fluorine momentum in
Journal of Physics: Condensed Matter. 2012. DOI : 10.1088/0953-8984/24/36/365401.The Fuzzy Quantum Proton in the Hydrogen Chloride Hydrates
Journal of the American Chemical Society. 2012. DOI : 10.1021/ja3014727.Efficient First-Principles Calculation of the Quantum Kinetic Energy and Momentum Distribution of Nuclei
Physical Review Letters. 2012. DOI : 10.1103/PhysRevLett.109.100604.2011
Efficient multiple time scale molecular dynamics: Using colored noise thermostats to stabilize resonances
The Journal of Chemical Physics. 2011. DOI : 10.1063/1.3518369.Static disorder and structural correlations in the low-temperature phase of lithium imide
Physical Review B. 2011. DOI : 10.1103/PhysRevB.83.054119.First-Principles Study of the High-Temperature Phase of Li
The Journal of Physical Chemistry C. 2011. DOI : 10.1021/jp200076p.From the Cover: Simplifying the representation of complex free-energy landscapes using sketch-map
Proceedings of the National Academy of Sciences. 2011. DOI : 10.1073/pnas.1108486108.Accelerating the convergence of path integral dynamics with a generalized Langevin equation
The Journal of Chemical Physics. 2011. DOI : 10.1063/1.3556661.2010
Solid-liquid interface free energy through metadynamics simulations
Physical Review B. 2010. DOI : 10.1103/PhysRevB.81.125416.Colored-Noise Thermostats à la Carte
Journal of Chemical Theory and Computation. 2010. DOI : 10.1021/ct900563s.Nuclear quantum effects in ab initio dynamics: Theory and experiments for lithium imide
Physical Review B. 2010. DOI : 10.1103/PhysRevB.82.174306.The -thermostat: selective normal-modes excitation by colored-noise Langevin dynamics
Procedia Computer Science. 2010. DOI : 10.1016/j.procs.2010.04.180.Efficient stochastic thermostatting of path integral molecular dynamics
The Journal of Chemical Physics. 2010. DOI : 10.1063/1.3489925.A self-learning algorithm for biased molecular dynamics
Proceedings of the National Academy of Sciences. 2010. DOI : 10.1073/pnas.1011511107.2009
Nuclear Quantum Effects in Solids Using a Colored-Noise Thermostat
Physical Review Letters. 2009. DOI : 10.1103/PhysRevLett.103.030603.Langevin Equation with Colored Noise for Constant-Temperature Molecular Dynamics Simulations
Physical Review Letters. 2009. DOI : 10.1103/PhysRevLett.102.020601.Ab initio study of the diffusion and decomposition pathways of SiHx species on Si(100)
Physical Review B. 2009. DOI : 10.1103/PhysRevB.79.165437.2008
An efficient and accurate decomposition of the Fermi operator
The Journal of Chemical Physics. 2008. DOI : 10.1063/1.2949515.First principles study of Ge∕Si exchange mechanisms at the Si(001) surface
Applied Physics Letters. 2008. DOI : 10.1063/1.2926683.2007
Quantitative estimate of H abstraction by thermal SiH3 on hydrogenated Si(001)(2×1)
Physical Review B. 2007. DOI : 10.1103/PhysRevB.75.235311.Diffusion and desorption of SiH3 on hydrogenated H:Si(100)-(2×1) from first principles
Physical Review B. 2007. DOI : 10.1103/PhysRevB.76.245309.Conjugate gradient heat bath for ill-conditioned actions
Physical Review E. 2007. DOI : 10.1103/PhysRevE.76.026707.2006
Impact-driven effects in thin-film growth: steering and transient mobility at the Ag(110) surface
Nanotechnology. 2006. DOI : 10.1088/0957-4484/17/14/033.Ab initio study of the vibrational properties of crystalline TeO2: The α, β, and γ phases
Physical Review B. 2006. DOI : 10.1103/PhysRevB.73.104304.Teaching & PhD
Teaching
Materials Science and Engineering
PhD Students
Abbott Joseph William, Bigi Filippo, How Wei Bin, Huguenin-Dumittan Kevin Kazuki, Kellner Matthias Linus, Mazitov Arslan, Nigam Jigyasa, Pozdnyakov Sergey, Rumiantsev Egor, Saade Sandra, Suman Divya,Past EPFL PhD Students
Anelli Andrea , Baldi Edoardo , Ben Mahmoud Chiheb , Cheng Bingqing , Gasparotto Piero , Giofré Daniele , Goscinski Alexander Jan , Grisafi Andrea , Helfrecht Benjamin Aaron , Imbalzano Giulio , Kapil Venkat , Lopanitsyna Nataliya , Maksimov Dmitrii , Musil Félix Benedito Clément ,Courses
Introduction to atomic-scale modeling
This course provides an introduction to the modeling of matter at the atomic scale, using interactive Jupyter notebooks to see several of the core concepts of materials science in action.
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)
Lecture series on scientific machine learning
Machine learning is a data analysis and computational tool that in the last two decades brought groundbreaking progress into many modern technologies. What is more, machine learning is becoming an indispensable tool enabling progress in many scientific disciplines where knowledge is deduced from data.
This course will present some recent works in this direction. In the first part of the