Félix Musil
Cited by
Cited by
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
Chemical shifts in molecular solids by machine learning
FM Paruzzo, A Hofstetter, F Musil, S De, M Ceriotti, L Emsley
Nature communications 9 (1), 4501, 2018
Machine learning for the structure–energy–property landscapes of molecular crystals
F Musil, S De, J Yang, JE Campbell, GM Day, M Ceriotti
Chemical science 9 (5), 1289-1300, 2018
Atom-density representations for machine learning
MJ Willatt, F Musil, M Ceriotti
The Journal of chemical physics 150 (15), 154110, 2019
Fast and Accurate Uncertainty Estimation in Chemical Machine Learning
F Musil, MJ Willatt, MA Langovoy, M Ceriotti
Journal of chemical theory and computation 15 (2), 906-915, 2019
Feature optimization for atomistic machine learning yields a data-driven construction of the periodic table of the elements
MJ Willatt, F Musil, M Ceriotti
Physical Chemistry Chemical Physics 20 (47), 29661-29668, 2018
The GBS code for tokamak scrape-off layer simulations
FD Halpern, P Ricci, S Jolliet, J Loizu, J Morales, A Mosetto, F Musil, ...
Journal of Computational Physics 315, 388-408, 2016
Efficient implementation of atom-density representations
F Musil, M Veit, A Goscinski, G Fraux, MJ Willatt, M Stricker, T Junge, ...
The Journal of Chemical Physics 154 (11), 114109, 2021
Mapping and classifying molecules from a high-throughput structural database
S De, F Musil, T Ingram, C Baldauf, M Ceriotti
Journal of cheminformatics 9 (1), 1-14, 2017
Data science based Mg corrosion engineering
T Würger, C Feiler, F Musil, GBV Feldbauer, D Höche, SV Lamaka, ...
Frontiers in Materials 6, 53, 2019
Machine learned coarse-grained protein force-fields: Are we there yet?
AEP Durumeric, NE Charron, C Templeton, F Musil, K Bonneau, ...
Current Opinion in Structural Biology 79, 102533, 2023
Blob properties in full-turbulence simulations of the TCV scrape-off layer
F Nespoli, I Furno, B Labit, P Ricci, F Avino, FD Halpern, F Musil, F Riva
Plasma Physics and Controlled Fusion 59 (5), 055009, 2017
Optimal radial basis for density-based atomic representations
A Goscinski, F Musil, S Pozdnyakov, J Nigam, M Ceriotti
The Journal of Chemical Physics 155 (10), 104106, 2021
Quantum dynamics using path integral coarse-graining
F Musil, I Zaporozhets, F Noé, C Clementi, V Kapil
The Journal of Chemical Physics 157 (18), 181102, 2022
Machine Learning at the Atomic Scale
F Musil, M Ceriotti
CHIMIA International Journal for Chemistry 73 (12), 972-982, 2019
The impact of the Boussinesq approximation on plasma turbulence in the scrape-off layer
J Morales, BJ Frei, F Halpern, F Musil, P Paruta, P Ricci, F Riva, M Siffert, ...
43rd European Physical Society Conference on Plasma Physics, 2016
Navigating protein landscapes with a machine-learned transferable coarse-grained model
NE Charron, F Musil, A Guljas, Y Chen, K Bonneau, AS Pasos-Trejo, ...
arXiv preprint arXiv:2310.18278, 2023
A general and efficient framework for atomistic machine learning
FBC Musil
EPFL, 2021
Advancing SOL simulations: avoiding the Boussinesq approximation and coupling closed and open magnetic flux surfaces
JA Morales Mena, BJ Frei, F Halpern, F Musil, P Paruta, P Ricci, F Riva, ...
21st Joint EU-US Transport Task Force Meeting, Leysin, Switzerland, 5-8 …, 2016
The impact of the Boussinesq approximation on the simulation of scrape-off layer plasma turbulence
F Musil
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