3 papers
cond-mat.mtrl-sci2026
Neural surrogates for crystal growth dynamics with variable supersaturation: explicit vs. implicit conditioning
Matteo Rigoni, Daniele Lanzoni, Francesco Montalenti +1
Simulations of crystal growth are performed by using Convolutional Recurrent Neural Network surrogate models, trained on a dataset of time sequences computed by numerical integrati…
cond-mat.stat-mech2025
Learning Kinetic Monte Carlo stochastic dynamics with Deep Generative Adversarial Networks
Daniele Lanzoni, Olivier Pierre-Louis, Roberto Bergamaschini +1
We show that Generative Adversarial Networks (GANs) may be fruitfully exploited to learn stochastic dynamics, surrogating traditional models while capturing thermal fluctuations. S…
cond-mat.mtrl-sci2025
Unified machine-learning framework for property prediction and time-evolution simulation of strained alloy microstructure
Andrea Fantasia, Daniele Lanzoni, Niccolò Di Eugenio +3
We introduce a unified machine-learning framework designed to conveniently tackle the temporal evolution of alloy microstructures under the influence of an elastic field. This appr…