| 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 | 478 | 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 | 248 | 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 | 220 | 2018 |
| Atom-density representations for machine learning MJ Willatt, F Musil, M Ceriotti The Journal of chemical physics 150 (15), 154110, 2019 | 187 | 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 | 167 | 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 | 142 | 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 | 128 | 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 | 58 | 2021 |
| 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 | 55 | 2023 |
| 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 | 48 | 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 | 44 | 2019 |
| 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 | 34 | 2017 |
| 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 | 33 | 2022 |
| 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 | 28 | 2021 |
| 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 | 13 | 2023 |
| Machine Learning at the Atomic Scale F Musil, M Ceriotti CHIMIA International Journal for Chemistry 73 (12), 972-982, 2019 | 10 | 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 | 4 | 2016 |
| A general and efficient framework for atomistic machine learning FBC Musil EPFL, 2021 | | 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 | | 2016 |
| The impact of the Boussinesq approximation on the simulation of scrape-off layer plasma turbulence F Musil | | 2015 |
Michele CeriottiAssociate Professor at EPFL, Institute of MaterialsEmail verificata su epfl.ch