Michele Ceriotti
Michele Ceriotti
Associate Professor at EPFL, Institute of Materials
Verified email at - 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
Promoting transparency and reproducibility in enhanced molecular simulations
Nature methods 16 (8), 670-673, 2019
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
Gaussian process regression for materials and molecules
VL Deringer, AP Bartók, N Bernstein, DM Wilkins, M Ceriotti, G Csányi
Chemical Reviews 121 (16), 10073-10141, 2021
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
Physics-inspired structural representations for molecules and materials
F Musil, A Grisafi, AP Bartók, C Ortner, G Csányi, M Ceriotti
Chemical Reviews 121 (16), 9759-9815, 2021
Efficient stochastic thermostatting of path integral molecular dynamics
M Ceriotti, M Parrinello, TE Markland, DE Manolopoulos
The Journal of chemical physics 133, 124104, 2010
Nuclear quantum effects enter the mainstream
TE Markland, M Ceriotti
Nature Reviews Chemistry 2 (3), 0109, 2018
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
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
i-PI 2.0: A universal force engine for advanced molecular simulations
V Kapil, M Rossi, O Marsalek, R Petraglia, Y Litman, T Spura, B Cheng, ...
Computer Physics Communications 236, 214-223, 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
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
Nuclear quantum effects in solids using a colored-noise thermostat
M Ceriotti, G Bussi, M Parrinello
Physical review letters 103 (3), 030603, 2009
Symmetry-Adapted Machine-Learning for Tensorial Properties of Atomistic Systems
A Grisafi, DM Wilkins, G Csányi, M Ceriotti
Physical Review Letters 120, 036002, 2018
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
Origins of structural and electronic transitions in disordered silicon
VL Deringer, N Bernstein, G Csányi, C Ben Mahmoud, M Ceriotti, ...
Nature 589 (7840), 59-64, 2021
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
A transferable machine-learning model of the electron density
A Grisafi, DM Wilkins, BAR Meyer, A Fabrizio, C Corminboeuf, M Ceriotti
ACS Central Science 5, 57, 2019
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