activity
20242026
most citedPolytypic Quantum Wells in Si and Ge: Impact of 2D Hexagonal Inclusions on Electronic Band Structure

6 citations · 14 across the 10 of their papers we have counts for

collaborators

11 papers

cond-mat.mtrl-sci2026

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…

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.mtrl-sci2026

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…

cond-mat.mtrl-sci2026

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…

cond-mat.mtrl-sci2026

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…

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…