Ab initio phase diagram and nucleation of gallium H Niu, L Bonati, PM Piaggi, M Parrinello Nature communications 11 (1), 2654, 2020 | 161 | 2020 |
Silicon Liquid Structure and Crystal Nucleation from Ab Initio Deep Metadynamics L Bonati, M Parrinello Physical review letters 121 (26), 265701, 2018 | 146 | 2018 |
Data-driven collective variables for enhanced sampling L Bonati, V Rizzi, M Parrinello The journal of physical chemistry letters 11 (8), 2998-3004, 2020 | 130 | 2020 |
Deep learning the slow modes for rare events sampling L Bonati, GM Piccini, M Parrinello Proceedings of the National Academy of Sciences 118 (44), e2113533118, 2021 | 127 | 2021 |
Neural networks-based variationally enhanced sampling L Bonati, YY Zhang, M Parrinello Proceedings of the National Academy of Sciences 116 (36), 17641-17647, 2019 | 124 | 2019 |
Using metadynamics to build neural network potentials for reactive events: the case of urea decomposition in water M Yang, L Bonati, D Polino, M Parrinello Catalysis Today 387, 143-149, 2022 | 100 | 2022 |
The role of water in host-guest interaction V Rizzi, L Bonati, N Ansari, M Parrinello Nature Communications 12 (1), 1-7, 2021 | 49 | 2021 |
A unified framework for machine learning collective variables for enhanced sampling simulations: mlcolvar L Bonati, E Trizio, A Rizzi, M Parrinello The Journal of Chemical Physics 159 (1), 2023 | 24 | 2023 |
Characterizing metastable states with the help of machine learning P Novelli, L Bonati, M Pontil, M Parrinello Journal of Chemical Theory and Computation 18 (9), 5195-5202, 2022 | 17 | 2022 |
The role of dynamics in heterogeneous catalysis: Surface diffusivity and N2 decomposition on Fe(111) L Bonati, D Polino, C Pizzolitto, P Biasi, R Eckert, S Reitmeier, R Schlögl, ... Proceedings of the National Academy of Sciences 120 (50), e2313023120, 2023 | 14 | 2023 |
Unraveling the crystallization kinetics of the Ge2Sb2Te5 phase change compound with a machine-learned interatomic potential O Abou El Kheir, L Bonati, M Parrinello, M Bernasconi npj Computational Materials 10 (1), 33, 2024 | 8 | 2024 |
How Poisoning Is Avoided in a Step of Relevance to the Haber–Bosch Catalysis S Tripathi, L Bonati, S Perego, M Parrinello ACS Catalysis 14 (7), 4944-4950, 2024 | 4 | 2024 |
Non-linear temperature dependence of nitrogen adsorption and decomposition on Fe (111) surface L Bonati, D Polino, C Pizzolitto, P Biasi, R Eckert, S Reitmeier, R Schlögl, ... | 3 | 2023 |
Deep learning path-like collective variable for enhanced sampling molecular dynamics T Fröhlking, L Bonati, V Rizzi, FL Gervasio The Journal of Chemical Physics 160 (17), 2024 | 2 | 2024 |
Combining transition path sampling with data-driven collective variables through a reactivity-biased shooting algorithm J Zhang, O Zhang, L Bonati, TJ Hou Journal of Chemical Theory and Computation, 2024 | 1 | 2024 |
Transfer learning for atomistic simulations using GNNs and kernel mean embeddings J Falk, L Bonati, P Novelli, M Parrinello, M Pontil Advances in Neural Information Processing Systems 36, 2024 | 1 | 2024 |
Machine learning and enhanced sampling simulations L Bonati ETH Zurich, 2021 | 1 | 2021 |
Data-efficient modeling of catalytic reactions via enhanced sampling and on-the-fly learning of machine learning potentials S Perego, L Bonati | | 2024 |
How dynamics changes ammonia cracking on iron surfaces S Perego, L Bonati, S Tripathi, M Parrinello | | 2024 |
Training collective variables for enhanced sampling via neural networks based discriminant analysis L Bonati IL NUOVO CIMENTO C 44, 125, 2021 | | 2021 |