| Predicting molecular dipole moments by combining atomic partial charges and atomic dipoles M Veit, DM Wilkins, Y Yang, RA DiStasio Jr, M Ceriotti The Journal of Chemical Physics 153 (2), 024113, 2020 | 38 | 2020 |
| Equation of state of fluid methane from first principles with machine learning potentials M Veit, SK Jain, S Bonakala, I Rudra, D Hohl, G Csányi Journal of chemical theory and computation 15 (4), 2574-2586, 2019 | 38 | 2019 |
| 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 | 20 | 2021 |
| Multi-scale electrolyte transport simulations for lithium ion batteries F Hanke, N Modrow, RLC Akkermans, I Korotkin, FC Mocanu, VA Neufeld, ... Journal of The Electrochemical Society 167 (1), 013522, 2019 | 11 | 2019 |
| LIBRASCAL F Musil, M Veit, T Junge, M Stricker, A Goscinki, G Fraux, M Ceriotti GitHub, https://github. com/cosmo-epfl/librascal, 2018 | 5 | 2018 |
| Eigenfunction decomposition of reactor perturbations and transitions using MCNP Monte Carlo C Josey, MD Veit Los Alamos National Lab.(LANL), Los Alamos, NM (United States), 2013 | 5 | 2013 |
| Roadmap on machine learning in electronic structure H Kulik, T Hammerschmidt, J Schmidt, S Botti, MAL Marques, M Boley, ... Electronic Structure, 2022 | 2 | 2022 |
| Bulk methane models and simulation parameters M Veit | 1 | 2018 |
| Locality of forces in molecular systems M Veit University of Cambridge, 2015 | 1 | 2015 |
| Dielectric response of BaTiO3 from an integrated machine learning model M Veit, L Gigli, M Kotiuga, G Pizzi, N Marzari, M Ceriotti Bulletin of the American Physical Society, 2022 | | 2022 |
| Thermodynamics and dielectric response of by data-driven modeling L Gigli, M Veit, M Kotiuga, G Pizzi, N Marzari, M Ceriotti arXiv preprint arXiv:2111.05129, 2021 | | 2021 |
| Machine learning the molecular dipole moment with atomic partial charges and atomic dipoles M Veit, D Wilkins, Y Yang, RA DiStasio Jr., M Ceriotti Bulletin of the American Physical Society, 2021 | | 2021 |
| Representative trajectory data supporting" Multiscale Electrolyte Transport Simulations for Lithium Ion Batteries" F Hanke, N Modrow, RLC Akkermans, I Korotkin, FC Mocanu, V Neufeld, ... | | 2020 |
| Research data supporting" Designing a machine learning potential for molecular simulation of liquid alkanes" M Veit | | 2019 |
| Designing a machine learning potential for molecular simulation of liquid alkanes M Veit University of Cambridge, 2018 | | 2018 |
| Stochastic Simulation of Genetic Regulatory Networks with Delayed Reactions M Veit | | 2014 |
| Section 2.6-Integrated machine learning models: electronic structure accuracy beyond local potentials M Veit, A Grisafi, J Nigam, M Ceriotti Roadmap on Machine Learning in Electronic Structure, 47, 0 | | |
| Group ID U12743 Affiliated authors Anelli, Andrea E Baldi, B Cheng, S De, EA Engel, G Fraux, P Gasparotto, F Giberti, ... | | |
| Predicting molecular dipoles by combining atomic partial charges and atomic dipoles M Veit, DM Wilkins, Y Yang, RA DiStasio Jr, M Ceriotti | | |
Michele CeriottiAssociate Professor at EPFL, Institute of MaterialsVerified email at epfl.ch
