LANZONI, DANIELE
LANZONI, DANIELE
DIPARTIMENTO DI SCIENZA DEI MATERIALI
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Computational analysis of low-energy dislocation configurations in graded layers
2020 Lanzoni, D; Rovaris, F; Montalenti, F
A machine learning approach for studying low-energy dislocation distributions: methodology and applications to Ge/Si(001) films
2021 Lanzoni, D; Rovaris, F; Montalenti, F
Morphological evolution via surface diffusion learned by convolutional, recurrent neural networks: Extrapolation and prediction uncertainty
2022 Lanzoni, D; Albani, M; Bergamaschini, R; Montalenti, F
Machine learning potential for interacting dislocations in the presence of free surfaces
2022 Lanzoni, D; Rovaris, F; Montalenti, F
| Titolo | Tipologia | Data di pubblicazione | Autori | File |
|---|---|---|---|---|
| Computational analysis of low-energy dislocation configurations in graded layers | 01 - Articolo su rivista | 2020 | Lanzoni D.Rovaris F.Montalenti F. | |
| A machine learning approach for studying low-energy dislocation distributions: methodology and applications to Ge/Si(001) films | 02 - Intervento a convegno | 2021 | Lanzoni, DRovaris, FMontalenti, F | |
| Morphological evolution via surface diffusion learned by convolutional, recurrent neural networks: Extrapolation and prediction uncertainty | 01 - Articolo su rivista | 2022 | Daniele LanzoniMarco AlbaniRoberto BergamaschiniFrancesco Montalenti | |
| Machine learning potential for interacting dislocations in the presence of free surfaces | 01 - Articolo su rivista | 2022 | Lanzoni D.Rovaris F.Montalenti F. |