6 citations · 14 across the 10 of their papers we have counts for
11 papers
Growth and characterization of planar hexagonal Ge on CdS
Andrea Besana, Veronica Regazzoni, Marco Faverzani +13
Hexagonal group-IV semiconductors have attracted increasing interest owing to their unconventional electronic and optical properties compared to the cubic diamond phase. However, t…
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…
Resolving the Metastable Si-XIII Structure through Convergent Theory and Experiment
Fabrizio Rovaris, Corrado Bongiorno, Anna Marzegalli +10
Silicon is the undisputed cornerstone of modern technology, with applications ranging from micro- and opto-electronics to quantum technologies. Recently, the exploration of its all…
Electronic States, Spin-Orbit Coupling and Magnetism in Germanium 60° Dislocations
Veronica Regazzoni, Fabrizio Rovaris, Anna Marzegalli +2
Defects in semiconductors have recently attracted renewed interest owing to their potential in novel quantum applications. Here we investigate the electronic and magnetic propertie…
Cross-hatch strain effects on SiGe quantum dots for qubit variability estimation
Luis Fabián Peña, Mitchell I. Brickson, Fabrizio Rovaris +11
SiGe heterostructures integrated with Si via virtual substrate (VS) growth are promising hosts for spin qubits. While VS growth targets plastic relaxation, residual cross-hatch str…
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…
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…
Unraveling the Atomic-Scale Pathways Driving Pressure-Induced Phase Transitions in Silicon
Fabrizio Rovaris, Anna Marzegalli, Francesco Montalenti +1
Silicon exhibits several metastable allotropes which recently attracted attention in the quest for materials with superior (e.g. optical) properties, compatible with Si technology.…
Polytypic Quantum Wells in Si and Ge: Impact of 2D Hexagonal Inclusions on Electronic Band Structure
Anna Marzegalli, Francesco Montalenti, Emilio Scalise
Crystal defects, traditionally viewed as detrimental, are now being explored for quantum technology applications. This study focuses on stacking faults in silicon and germanium, fo…
Extreme time extrapolation capabilities and thermodynamic consistency of physics-inspired Neural Networks for the 3D microstructure evolution of materials via Cahn-Hilliard flow
Daniele Lanzoni, Andrea Fantasia, Roberto Bergamaschini +2
A Convolutional Recurrent Neural Network (CRNN) is trained to reproduce the evolution of the spinodal decomposition process in three dimensions as described by the Cahn-Hilliard eq…
Quantitative analysis of the prediction performance of a Convolutional Neural Network evaluating the surface elastic energy of a strained film
Luis Martín Encinar, Daniele Lanzoni, Andrea Fantasia +3
A Deep Learning approach is devised to estimate the elastic energy density at the free surface of an undulated stressed film. About 190000 arbitrary surface profiles h(x) are r…