| 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 | 267 | 2018 |
| Electron density learning of non-covalent systems A Fabrizio, A Grisafi, B Meyer, M Ceriotti, C Corminboeuf Chemical science 10 (41), 9424-9432, 2019 | 151 | 2019 |
| Open-shell nonbenzenoid nanographenes containing two pairs of pentagonal and heptagonal rings J Liu, S Mishra, CA Pignedoli, D Passerone, JI Urgel, A Fabrizio, TG Lohr, ... Journal of the American Chemical Society 141 (30), 12011-12020, 2019 | 129 | 2019 |
| The role of bridging ligands in dinitrogen reduction and functionalization by uranium multimetallic complexes M Falcone, L Barluzzi, J Andrez, F Fadaei Tirani, I Zivkovic, A Fabrizio, ... Nature chemistry 11 (2), 154-160, 2019 | 123 | 2019 |
| Qualitatively incorrect features in the TDDFT spectrum of thiophene-based compounds A Prlj, BFE Curchod, A Fabrizio, L Floryan, C Corminboeuf The journal of physical chemistry letters 6 (1), 13-21, 2015 | 83 | 2015 |
| Rationalizing fluorescence quenching in meso-BODIPY dyes A Prlj, A Fabrizio, C Corminboeuf Physical Chemistry Chemical Physics 18 (48), 32668-32672, 2016 | 64 | 2016 |
| Exploring the limitation of molecular water oxidation catalysts M Busch, A Fabrizio, S Luber, J Hutter, C Corminboeuf The Journal of Physical Chemistry C 122 (23), 12404-12412, 2018 | 46 | 2018 |
| Synthesis of aminyl biradicals by base-induced Csp 3–Csp 3 coupling of cationic azo dyes Y Liu, P Varava, A Fabrizio, LYM Eymann, AG Tskhovrebov, OM Planes, ... Chemical science 10 (22), 5719-5724, 2019 | 34 | 2019 |
| Tuning the structure, reactivity and magnetic communication of nitride-bridged uranium complexes with the ancillary ligands CT Palumbo, L Barluzzi, R Scopelliti, I Zivkovic, A Fabrizio, ... Chemical science 10 (38), 8840-8849, 2019 | 33 | 2019 |
| Data-powered augmented volcano plots for homogeneous catalysis MD Wodrich, A Fabrizio, B Meyer, C Corminboeuf Chemical Science 11 (44), 12070-12080, 2020 | 32 | 2020 |
| Physics-based representations for machine learning properties of chemical reactions P van Gerwen, A Fabrizio, MD Wodrich, C Corminboeuf Machine Learning: Science and Technology 3 (4), 045005, 2022 | 29 | 2022 |
| OSCAR: an extensive repository of chemically and functionally diverse organocatalysts S Gallarati, P van Gerwen, R Laplaza, S Vela, A Fabrizio, C Corminboeuf Chemical Science 13 (46), 13782-13794, 2022 | 26 | 2022 |
| How do London dispersion interactions impact the photochemical processes of molecular switches? A Fabrizio, C Corminboeuf The Journal of Physical Chemistry Letters 9 (3), 464-470, 2018 | 25 | 2018 |
| Data mining the C− C cross‐coupling genome B Sawatlon, MD Wodrich, B Meyer, A Fabrizio, C Corminboeuf ChemCatChem 11 (16), 4096-4107, 2019 | 20 | 2019 |
| Hamiltonian-reservoir replica exchange and machine learning potentials for computational organic chemistry R Fabregat, A Fabrizio, B Meyer, D Hollas, C Corminboeuf Journal of chemical theory and computation 16 (5), 3084-3094, 2020 | 19 | 2020 |
| Synthesis and characterization of semiaromatic polyamides comprising benzofurobenzofuran repeating units J Cretenoud, B Özen, T Schmaltz, D Görl, A Fabrizio, C Corminboeuf, ... Polymer Chemistry 8 (14), 2197-2209, 2017 | 17 | 2017 |
| Machine learning models of the energy curvature vs particle number for optimal tuning of long-range corrected functionals A Fabrizio, B Meyer, C Corminboeuf The Journal of chemical physics 152 (15), 2020 | 15 | 2020 |
| SPA HM: The spectrum of approximated Hamiltonian matrices representations A Fabrizio, KR Briling, C Corminboeuf Digital Discovery 1 (3), 286-294, 2022 | 13 | 2022 |
| Learning on-top: Regressing the on-top pair density for real-space visualization of electron correlation A Fabrizio, KR Briling, DD Girardier, C Corminboeuf The Journal of Chemical Physics 153 (20), 2020 | 12 | 2020 |
| Quantum chemistry meets machine learning A Fabrizio, B Meyer, R Fabregat, C Corminboeuf Chimia 73 (12), 983-983, 2019 | 12 | 2019 |
