| Automatic selection of atomic fingerprints and reference configurations for machine-learning potentials G Imbalzano, A Anelli, D Giofré, S Klees, J Behler, M Ceriotti The Journal of chemical physics 148 (24), 2018 | 313 | 2018 |
| Comparison of permutationally invariant polynomials, neural networks, and Gaussian approximation potentials in representing water interactions through many-body expansions TT Nguyen, E Székely, G Imbalzano, J Behler, G Csányi, M Ceriotti, ... The Journal of chemical physics 148 (24), 2018 | 199 | 2018 |
| Uncertainty estimation for molecular dynamics and sampling G Imbalzano, Y Zhuang, V Kapil, K Rossi, EA Engel, F Grasselli, M Ceriotti The Journal of Chemical Physics 154 (7), 074102, 2021 | 75 | 2021 |
| The role of feature space in atomistic learning A Goscinski, G Fraux, G Imbalzano, M Ceriotti Machine Learning: Science and Technology 2 (2), 025028, 2021 | 34 | 2021 |
| Modeling the Ga/As binary system across temperatures and compositions from first principles G Imbalzano, M Ceriotti Physical Review Materials 5 (6), 063804, 2021 | 17 | 2021 |
| 3D ordering at the liquid–solid polar interface of nanowires M Zamani, G Imbalzano, N Tappy, DTL Alexander, S Martí‐Sánchez, ... Advanced Materials 32 (38), 2001030, 2020 | 16 | 2020 |
| First principle calculations of the residual resistivity of defects in metals G Imbalzano | 5 | 2015 |
| Transferable machine-learning models of complex materials: the case of GaAs G Imbalzano EPFL, 2021 | | 2021 |
| Group ID U12743 A Anelli, E Baldi, B Mahmoud, F Chiheb Bigi, M Ceriotti, R Cersonsky, ... | | |
Michele CeriottiAssociate Professor at EPFL, Institute of Materials在 epfl.ch 的电子邮件经过验证
