| 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 | 355 | 2021 |
| Symmetry-adapted machine learning for tensorial properties of atomistic systems A Grisafi, DM Wilkins, G Csányi, M Ceriotti Physical review letters 120 (3), 036002, 2018 | 263 | 2018 |
| Transferable machine-learning model of the electron density A Grisafi, A Fabrizio, B Meyer, DM Wilkins, C Corminboeuf, M Ceriotti ACS central science 5 (1), 57-64, 2018 | 243 | 2018 |
| Accurate molecular polarizabilities with coupled cluster theory and machine learning DM Wilkins, A Grisafi, Y Yang, KU Lao, RA DiStasio Jr, M Ceriotti Proceedings of the National Academy of Sciences 116 (9), 3401-3406, 2019 | 175 | 2019 |
| Incorporating long-range physics in atomic-scale machine learning A Grisafi, M Ceriotti The Journal of chemical physics 151 (20), 2019 | 149 | 2019 |
| Electron density learning of non-covalent systems A Fabrizio, A Grisafi, B Meyer, M Ceriotti, C Corminboeuf Chemical science 10 (41), 9424-9432, 2019 | 135 | 2019 |
| Roadmap on machine learning in electronic structure HJ Kulik, T Hammerschmidt, J Schmidt, S Botti, MAL Marques, M Boley, ... Electronic Structure 4 (2), 023004, 2022 | 102 | 2022 |
| Using Gaussian process regression to simulate the vibrational Raman spectra of molecular crystals N Raimbault, A Grisafi, M Ceriotti, M Rossi New Journal of Physics 21 (10), 105001, 2019 | 62 | 2019 |
| Multi-scale approach for the prediction of atomic scale properties A Grisafi, J Nigam, M Ceriotti Chemical science 12 (6), 2078-2090, 2021 | 46 | 2021 |
| Solvent fluctuations and nuclear quantum effects modulate the molecular hyperpolarizability of water C Liang, G Tocci, DM Wilkins, A Grisafi, S Roke, M Ceriotti Physical Review B 96 (4), 041407, 2017 | 39 | 2017 |
| Quantum mechanical static dipole polarizabilities in the QM7b and AlphaML showcase databases Y Yang, KU Lao, DM Wilkins, A Grisafi, M Ceriotti, RA DiStasio Jr Scientific data 6 (1), 152, 2019 | 36 | 2019 |
| Learning electron densities in the condensed phase AM Lewis, A Grisafi, M Ceriotti, M Rossi Journal of chemical theory and computation 17 (11), 7203-7214, 2021 | 31 | 2021 |
| Atomic-scale representation and statistical learning of tensorial properties A Grisafi, DM Wilkins, MJ Willatt, M Ceriotti Machine Learning in Chemistry: Data-Driven Algorithms, Learning Systems, and …, 2019 | 17 | 2019 |
| Electronic-structure properties from atom-centered predictions of the electron density A Grisafi, AM Lewis, M Rossi, M Ceriotti Journal of Chemical Theory and Computation 19 (14), 4451-4460, 2022 | 13 | 2022 |
| Machine learning in chemistry: data-driven algorithms, learning systems, and predictions A Grisafi, D Wilkins, M Willatt, M Ceriotti American Chemical Society, 2019 | 12 | 2019 |
| Learning (from) the Electron Density: Transferability, Conformational and Chemical Diversity A Fabrizio, K Briling, A Grisafi, C Corminboeuf Chimia 74 (4), 232-232, 2020 | 10 | 2020 |
| Predicting the charge density response in metal electrodes A Grisafi, A Bussy, M Salanne, R Vuilleumier Physical Review Materials 7 (12), 125403, 2023 | 6 | 2023 |
| Effect of a temperature gradient on the screening properties of ionic fluids A Grisafi, F Grasselli Physical Review Materials 7 (4), 045803, 2023 | 3 | 2023 |
| Localized Polycentric Orbital Basis Set for Quantum Monte Carlo Calculations Derived from the Decomposition of Kohn-Sham Optimized Orbitals C Amovilli, FM Floris, A Grisafi Computation 4 (1), 10, 2016 | 3 | 2016 |
| Physics-enhanced machine learning with symmetry-adapted and long-range representations A Grisafi EPFL, 2021 | | 2021 |
Michele CeriottiAssociate Professor at EPFL, Institute of MaterialsVerified email at epfl.ch
