Michele Ceriotti
Michele Ceriotti
Associate Professor at EPFL, Institute of Materials
Verified email at epfl.ch - Homepage
Cited by
Cited by
Comparing molecules and solids across structural and alchemical space
S De, AP Bartók, G Csányi, M Ceriotti
Phys. Chem. Chem. Phys. 18, 13754, 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
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
Efficient stochastic thermostatting of path integral molecular dynamics
M Ceriotti, M Parrinello, TE Markland, DE Manolopoulos
The Journal of chemical physics 133, 124104, 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
Colored-noise thermostats ŕ la carte
M Ceriotti, G Bussi, M Parrinello
Journal of Chemical Theory and Computation 6 (4), 1170-1180, 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
Nuclear quantum effects in solids using a colored-noise thermostat
M Ceriotti, G Bussi, M Parrinello
Physical review letters 103 (3), 030603, 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
Langevin equation with colored noise for constant-temperature molecular dynamics simulations
M Ceriotti, G Bussi, M Parrinello
Physical review letters 102 (2), 20601, 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
Symmetry-Adapted Machine-Learning for Tensorial Properties of Atomistic Systems
A Grisafi, DM Wilkins, G Csányi, M Ceriotti
Physical Review Letters 120, 036002, 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
Nuclear quantum effects enter the mainstream
TE Markland, M Ceriotti
Nature Reviews Chemistry 2 (3), 1-14, 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
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
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
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
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
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
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