Michele Ceriotti

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michele parrinelloDepartment of ChemistryVerified email at phys.chem.ethz.ch
David M WilkinsQueen's University BelfastVerified email at qub.ac.uk
David ManolopoulosUniversity of OxfordVerified email at chem.ox.ac.uk
Gabor CsanyiProfessor of Molecular Modelling, Engineering Laboratory, University of CambridgeVerified email at cam.ac.uk
Gareth TribelloSchool of Maths and Physics, Queen's University BelfastVerified email at qub.ac.uk
Bingqing ChengAssistant Professor at IST AustriaVerified email at ist.ac.at
Andrea GrisafiPost-doctoral Research Fellow, Ecole normale supérieure, ParisVerified email at ens.psl.eu
Félix MusilPostDoc at FU BerlinVerified email at zedat.fu-berlin.de
Venkat KapilJunior Research Fellow, Churchill College, University of CambridgeVerified email at cam.ac.uk
Thomas MarklandAssociate Professor of Chemistry, Stanford UniversityVerified email at stanford.edu
Mariana RossiHead of Independent Lise Meitner Group, MPI for Structure and Dynamics of Matter, HamburgVerified email at mpsd.mpg.de
Sandip DeScientistVerified email at basf.com
marco bernasconiUniversity of Milano-BicoccaVerified email at unimib.it
Jörg BehlerGeorg-August-Universität GöttingenVerified email at uni-goettingen.de
Albert P. BartokUniversity of WarwickVerified email at warwick.ac.uk
E A EngelEcole Polytechnique Fédérale de LausanneVerified email at epfl.ch
Piero GasparottoPaul Scherrer InstituteVerified email at psi.ch
Giovanni BussiScuola Internazionale Superiore di Studi Avanzati, Trieste, ItalyVerified email at sissa.it
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

Atomic-scale modeling Machine learning Materials science Statistical mechanics Physical chemistry


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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	420	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	419	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	380	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	273	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	258	2011
Colored-noise thermostats à la carte M Ceriotti, G Bussi, M Parrinello Journal of Chemical Theory and Computation 6 (4), 1170-1180, 2010	236	2010
Promoting transparency and reproducibility in enhanced molecular simulations M Bonomi Nature methods 16 (8), 670-673, 2019	229	2019
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	226	2014
Nuclear quantum effects in solids using a colored-noise thermostat M Ceriotti, G Bussi, M Parrinello Physical review letters 103 (3), 030603, 2009	217	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	214	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	188	2009
Nuclear quantum effects enter the mainstream TE Markland, M Ceriotti Nature Reviews Chemistry 2 (3), 1-14, 2018	187	2018
Symmetry-Adapted Machine-Learning for Tensorial Properties of Atomistic Systems A Grisafi, DM Wilkins, G Csányi, M Ceriotti Physical Review Letters 120, 036002, 2018	170	2018
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	167	2018
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	163	2011
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	162	2014
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	158	2012
Ab initio thermodynamics of liquid and solid water B Cheng, EA Engel, J Behler, C Dellago, M Ceriotti Proceedings of the National Academy of Sciences 116 (4), 1110-1115, 2019	156	2019
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	146	2016
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	137	2006

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