Michele Ceriotti

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michele parrinelloDepartment of ChemistryVerified email at phys.chem.ethz.ch
David ManolopoulosUniversity of OxfordVerified email at chem.ox.ac.uk
David M WilkinsQueen's University BelfastVerified email at qub.ac.uk
Gareth TribelloSchool of Maths and Physics, Queen's University BelfastVerified email at qub.ac.uk
Gabor CsanyiProfessor of Molecular Modelling, Engineering Laboratory, University of CambridgeVerified email at cam.ac.uk
Bingqing ChengJunior research fellow at Trinity CollegeVerified email at cam.ac.uk
Andrea GrisafiPhD student, EPFLVerified email at epfl.ch
Venkat KapilResearch Fellow, Department of Chemistry, University of CambridgeVerified email at cam.ac.uk
Félix MusilPhD. student at EPFLVerified email at epfl.ch
Thomas MarklandAssociate Professor of Chemistry, Stanford UniversityVerified email at stanford.edu
marco bernasconiUniversity of Milano-BicoccaVerified email at unimib.it
Sandip DeScientistVerified email at basf.com
Mariana RossiHead of Independent Lise Meitner Group, MPI for Structure and Dynamics of Matter, HamburgVerified email at mpsd.mpg.de
Jörg BehlerGeorg-August-Universität GöttingenVerified email at uni-goettingen.de
Piero GasparottoEmpa - Swiss Federal Laboratories for Materials Science and TechnologyVerified email at empa.ch
Giovanni BussiScuola Internazionale Superiore di Studi Avanzati, Trieste, ItalyVerified email at sissa.it
E A EngelEcole Polytechnique Fédérale de LausanneVerified email at epfl.ch
Albert P. BartokUniversity of WarwickVerified email at warwick.ac.uk
Giulio ImbalzanoEPFLVerified email at epfl.ch
Sylvie RokeJulia Jacobi chair in photomedicine, Laboratory for fundamental BioPhotonics (LBP), EPFLVerified email at epfl.ch

Michele Ceriotti

Associate Professor at EPFL, Institute of Materials

Verified email at epfl.ch - Homepage

Atomistic simulations Machine Learning Materials Science Theoretical Chemistry Molecular Dynamics


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Comparing molecules and solids across structural and alchemical space S De, AP Bartók, G Csányi, M Ceriotti Phys. Chem. Chem. Phys. 18, 13754, 2016	337	2016
Machine Learning Unifies the Modelling of Materials and Molecules AP Bartok, S De, C Poelking, N Bernstein, J Kermode, G Csanyi, ... Science Advances 3 (12), e1701816, 2017	321	2017
Nuclear Quantum Effects in Water and Aqueous Systems: Experiment, Theory, and Current Challenges M Ceriotti, W Fang, PG Kusalik, RH McKenzie, A Michaelides, ... Chemical Reviews 116, 7529, 2016	315	2016
Efficient stochastic thermostatting of path integral molecular dynamics M Ceriotti, M Parrinello, TE Markland, DE Manolopoulos The Journal of chemical physics 133, 124104, 2010	234	2010
Simplifying the representation of complex free-energy landscapes using sketch-map M Ceriotti, GA Tribello, M Parrinello Proceedings of the National Academy of Sciences 108 (32), 13023-13028, 2011	227	2011
Colored-noise thermostats à la carte M Ceriotti, G Bussi, M Parrinello Journal of Chemical Theory and Computation 6 (4), 1170-1180, 2010	213	2010
i-PI: A Python interface for ab initio path integral molecular dynamics simulations M Ceriotti, J More, DE Manolopoulos Computer Physics Communications 185 (3), 1019-1026, 2014	204	2014
Nuclear quantum effects in solids using a colored-noise thermostat M Ceriotti, G Bussi, M Parrinello Physical review letters 103 (3), 030603, 2009	196	2009
Nuclear quantum effects and hydrogen bond fluctuations in water M Ceriotti, J Cuny, M Parrinello, DE Manolopoulos Proceedings of the National Academy of Sciences 110 (39), 15591-15596, 2013	190	2013
Langevin equation with colored noise for constant-temperature molecular dynamics simulations M Ceriotti, G Bussi, M Parrinello Physical review letters 102 (2), 20601, 2009	175	2009
Accelerating the convergence of path integral dynamics with a generalized Langevin equation M Ceriotti, DE Manolopoulos, M Parrinello The Journal of chemical physics 134, 084104, 2011	145	2011
Symmetry-Adapted Machine-Learning for Tensorial Properties of Atomistic Systems A Grisafi, DM Wilkins, G Csányi, M Ceriotti Physical Review Letters 120, 036002, 2018	141	2018
Efficient first-principles calculation of the quantum kinetic energy and momentum distribution of nuclei M Ceriotti, DE Manolopoulos Physical review letters 109 (10), 100604, 2012	139	2012
Nuclear quantum effects enter the mainstream TE Markland, M Ceriotti Nature Reviews Chemistry 2 (3), 1-14, 2018	134	2018
How to remove the spurious resonances from ring polymer molecular dynamics M Rossi, M Ceriotti, DE Manolopoulos The Journal of Chemical Physics 140, 234116, 2014	133	2014
Electrolytes induce long-range orientational order and free energy changes in the H-bond network of bulk water Y Chen, HI Okur, N Gomopoulos, C Macias-Romero, PS Cremer, ... Science advances 2 (4), e1501891, 2016	130	2016
A self-learning algorithm for biased molecular dynamics GA Tribello, M Ceriotti, M Parrinello Proceedings of the National Academy of Sciences 107 (41), 17509-17514, 2010	128	2010
Automatic Selection of Atomic Fingerprints and Reference Configurations for Machine-Learning Potentials G Imbalzano, A Anelli, S Klees, M Ceriotti The Journal of Chemical Physics 148, 241730, 2018	125	2018
Promoting transparency and reproducibility in enhanced molecular simulations M Bonomi, G Bussi, C Camilloni, GA Tribello, P Banáš, A Barducci, ... Nature methods 16 (8), 670-673, 2019	122	2019
Ab initio study of the vibrational properties of crystalline : The , , and phases M Ceriotti, F Pietrucci, M Bernasconi Physical Review B 73 (10), 104304, 2006	122	2006

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