| Silicon phase transitions in nanoindentation: Advanced molecular dynamics simulations with machine learning phase recognition G Ge, F Rovaris, D Lanzoni, L Barbisan, X Tang, L Miglio, A Marzegalli, ... Acta Materialia 263, 119465, 2024 | 5 | 2024 |
| Computational analysis of low-energy dislocation configurations in graded layers D Lanzoni, F Rovaris, F Montalenti Crystals 10 (8), 661, 2020 | 5 | 2020 |
| Morphological evolution via surface diffusion learned by convolutional, recurrent neural networks: Extrapolation and prediction uncertainty D Lanzoni, M Albani, R Bergamaschini, F Montalenti Physical Review Materials 6 (10), 103801, 2022 | 4 | 2022 |
| Machine learning potential for interacting dislocations in the presence of free surfaces D Lanzoni, F Rovaris, F Montalenti Scientific Reports 12 (1), 3760, 2022 | 4 | 2022 |
| Accurate generation of stochastic dynamics based on multi-model generative adversarial networks D Lanzoni, O Pierre-Louis, F Montalenti The Journal of Chemical Physics 159 (14), 2023 | 1 | 2023 |
| Quantitative analysis of the prediction performance of a Convolutional Neural Network evaluating the surface elastic energy of a strained film LM Encinar, D Lanzoni, A Fantasia, F Rovaris, R Bergamaschini, ... arXiv preprint arXiv:2405.03049, 2024 | | 2024 |
| Quantitative analysis of the prediction performance of a Convolutional Neural Network evaluating the surface elastic energy of a strained film L Martín Encinar, D Lanzoni, A Fantasia, F Rovaris, R Bergamaschini, ... arXiv e-prints, arXiv: 2405.03049, 2024 | | 2024 |
| Deep Learning methods for the investigation of temporal evolution of materials D Lanzoni Università degli Studi di Milano-Bicocca, 2024 | | 2024 |
| Learning and accurate generation of stochastic dynamics based on multi-model Generative Adversarial Networks D Lanzoni, O Pierre-Louis, F Montalenti arXiv preprint arXiv:2305.15920, 2023 | | 2023 |
| Author Correction: Machine learning potential for interacting dislocations in the presence of free surfaces D Lanzoni, F Rovaris, F Montalenti Scientific Reports 13, 2023 | | 2023 |
| A machine learning approach for studying low-energy dislocation distributions: methodology and applications to Ge/Si (001) films D Lanzoni, F Rovaris, F Montalenti | | 2021 |
