| Unsupervised learning methods for molecular simulation data A Glielmo, BE Husic, A Rodriguez, C Clementi, F Noé, A Laio Chemical Reviews 121 (16), 9722-9758, 2021 | 435 | 2021 |
| Accurate interatomic force fields via machine learning with covariant kernels A Glielmo, P Sollich, A De Vita Physical Review B 95 (21), 214302, 2017 | 271 | 2017 |
| Efficient nonparametric n-body force fields from machine learning A Glielmo, C Zeni, A De Vita Physical Review B 97 (18), 184307, 2018 | 177 | 2018 |
| SPONGE: A generalized eigenproblem for clustering signed networks M Cucuringu, P Davies, A Glielmo, H Tyagi Proceedings of Machine Learning Research 89, 1088-1098, 2019 | 104 | 2019 |
| Building machine learning force fields for nanoclusters C Zeni, K Rossi, A Glielmo, Á Fekete, N Gaston, F Baletto, A De Vita The Journal of chemical physics 148 (24), 2018 | 64 | 2018 |
| Ranking the information content of distance measures A Glielmo, C Zeni, B Cheng, G Csányi, A Laio PNAS Nexus 1 (2), pgac039, 2022 | 60 | 2022 |
| DADApy: Distance-based analysis of data-manifolds in Python A Glielmo, I Macocco, D Doimo, M Carli, C Zeni, R Wild, M d’Errico, ... Patterns 3 (10), 2022 | 56* | 2022 |
| On machine learning force fields for metallic nanoparticles C Zeni, K Rossi, A Glielmo, F Baletto Advances in Physics: X 4 (1), 1654919, 2019 | 50 | 2019 |
| Hierarchical nucleation in deep neural networks D Doimo, A Glielmo, A Laio, A Ansuini Advances in Neural Information Processing Systems 33 (NeurIPS 2020), 2020 | 49 | 2020 |
| Exploring the robust extrapolation of high-dimensional machine learning potentials C Zeni, A Anelli, A Glielmo, K Rossi Physical Review B 105 (16), 165141, 2022 | 35 | 2022 |
| Gaussian Process States: A data-driven representation of quantum many-body physics A Glielmo, Y Rath, G Csanyi, A De Vita, GH Booth Physical Review X 10 (4), 041026, 2020 | 34 | 2020 |
| Can we obtain the coefficient of restitution from the sound of a bouncing ball? M Heckel, A Glielmo, N Gunkelmann, T Pöschel Physical Review E 93 (3), 032901, 2016 | 31 | 2016 |
| Compact atomic descriptors enable accurate predictions via linear models C Zeni, K Rossi, A Glielmo, S De Gironcoli The Journal of Chemical Physics 154 (22), 2021 | 27 | 2021 |
| Reinforcement Learning for Combining Search Methods in the Calibration of Economic ABMs A Glielmo, M Favorito, D Chanda, DD Gatti Proceedings of the Fourth ACM International Conference on AI in Finance, 305–313, 2023 | 22 | 2023 |
| Coefficient of restitution of aspherical particles A Glielmo, N Gunkelmann, T Pöschel Physical Review E 90 (5), 052204, 2014 | 21 | 2014 |
| A Bayesian inference framework for compression and prediction of quantum states Y Rath, A Glielmo, GH Booth The Journal of chemical physics 153 (12), 2020 | 20 | 2020 |
| Black-it: A ready-to-use and easy-to-extend calibration kit for agent-based models M Benedetti, G Catapano, F De Sclavis, M Favorito, A Glielmo, ... Journal of Open Source Software 7 (79), 4622, 2022 | 19 | 2022 |
| Building Nonparametric n-Body Force Fields Using Gaussian Process Regression A Glielmo, C Zeni, A Fekete, A De Vita Machine Learning Meets Quantum Physics, 67-98, 2020 | 19 | 2020 |
| Simulating the Economic Impact of Rationality through Reinforcement Learning and Agent-Based Modelling S Brusatin, T Padoan, A Coletta, D Delli Gatti, A Glielmo Proceedings of the 5th ACM International Conference on AI in Finance, 159-167, 2024 | 18 | 2024 |
| Intrinsic dimension estimation for discrete metrics I Macocco, A Glielmo, J Grilli, A Laio Physical Review Letters 130 (6), 067401, 2023 | 17 | 2023 |
Claudio ZeniSenior Researcher @ MicrosoftVerified email at microsoft.com
