| 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 | 137 | 2022 |
| Predicting molecular dipole moments by combining atomic partial charges and atomic dipoles M Veit, DM Wilkins, Y Yang, RA DiStasio, M Ceriotti The Journal of chemical physics 153 (2), 2020 | 107 | 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 | 62 | 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), 2021 | 55 | 2021 |
| Thermodynamics and dielectric response of BaTiO3 by data-driven modeling L Gigli, M Veit, M Kotiuga, G Pizzi, N Marzari, M Ceriotti npj Computational Materials 8 (1), 209, 2022 | 26 | 2022 |
| 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 | 22 | 2019 |
| LIBRASCAL F Musil, M Veit, T Junge, M Stricker, A Goscinki, G Fraux, M Ceriotti | 5 | 2020 |
| Eigenfunction Decomposition of Reactor Perturbations & Transitions Using MCNP Monte Carlo C Josey, MD Veit Los Alamos National Laboratory (LANL), Los Alamos, NM (United States), 2013 | 5 | 2013 |
| Bulk methane models and simulation parameters M Veit | 1 | 2018 |
| Locality of forces in molecular systems M Veit | 1 | 2015 |
| Incorporating explicit electrostatic interactions in machine learning potentials M Veit, M Caro Bulletin of the American Physical Society, 2024 | | 2024 |
| Dielectric response of BaTiO3 from an integrated machine learning model M Veit, L Gigli, M Kotiuga, G Pizzi, N Marzari, M Ceriotti APS March Meeting Abstracts 2022, N69. 008, 2022 | | 2022 |
| 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, VA 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 | | 2018 |
| Stochastic Simulation of Genetic Regulatory Networks with Delayed Reactions M Veit | | 2014 |
| Boosting feed efficiency and reducing emissions: the influence of phytogenic additives. A Mader, M Veit | | 2010 |
| 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 A Anelli, E Baldi, B Mahmoud, F Chiheb Bigi, M Ceriotti, R Cersonsky, ... | | |
Michele CeriottiAssociate Professor at EPFL, Institute of MaterialsVerified email at epfl.ch
