FANTASIA, ANDREA
FANTASIA, ANDREA
DIPARTIMENTO DI SCIENZA DEI MATERIALI
Accelerating Crystal Growth Simulations by Convolutional Neural Networks
2024 Lanzoni, D; Martín-Encinar, L; Rovaris, F; Fantasia, A; Montalenti, F; Bergamaschini, R
Accelerating simulations of strained-film growth by deep learning: Finite element method accuracy over long time scales
2024 Lanzoni, D; Rovaris, F; Martín-Encinar, L; Fantasia, A; Bergamaschini, R; Montalenti, F
Convolutional Recurrent Neural Networks for tackling materials dynamics at the mesoscale
2024 Lanzoni, D; Bergamaschini, R; Fantasia, A; Montalenti, F
Development of a machine learning interatomic potential for exploring pressure-dependent kinetics of phase transitions in germanium
2024 Fantasia, A; Rovaris, F; Abou El Kheir, O; Marzegalli, A; Lanzoni, D; Pessina, L; Xiao, P; Zhou, C; Li, L; Henkelman, G; Scalise, E; Montalenti, F
Extreme time extrapolation capabilities and thermodynamic consistency of physics-inspired neural networks for the 3D microstructure evolution of materials via Cahn–Hilliard flow
2024 Lanzoni, D; Fantasia, A; Bergamaschini, R; Pierre-Louis, O; Montalenti, F
Simulating morphological evolutions by Convolutional Neural Networks
2024 Lanzoni, D; Rovaris, F; Fantasia, A; Martı́n-Encinar, L; Montalenti, F; Bergamaschini, R
Simulations of strained films evolution: extending accessible timescales through Convolutional Neural Networks
2024 Lanzoni, D; Rovaris, F; Martín-Encinar, L; Fantasia, A; Bergamaschini, R; Montalenti, F
Simulations of strained films evolution: extending accessible timescales through Convolutional Neural Networks
2024 Lanzoni, D; Rovaris, F; Martìn-Encinar, L; Bergamaschini, R; Fantasia, A; Montalenti, F
Unravelling Atomistic Mechanisms of Pressure-Induced Phase Transitions in Silicon Nanoindentation
2024 Rovaris, F; Marzegalli, A; Lanzoni, D; Fantasia, A; Ge, G; Montalenti, F; Scalise, E