33 citations · 77 across the 12 of their papers we have counts for
13 papers
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…
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…
Phase-field Modelling of Anisotropic Solid-State Dewetting on Patterned Substrates
Emma Radice, Marco Salvalaglio, Roberto Bergamaschini
We present a phase-field model for simulating the solid-state dewetting of anisotropic crystalline films on non-planar substrates. This model exploits two order parameters to trace…
Interface energies of Ga2O3 phases with the sapphire substrate and the phase-locked epitaxy of metastable structures explained
Ilaria Bertoni, Aldo Ugolotti, Emilio Scalise +2
Despite the extensive work carried out on the epitaxial growth of Ga2O3, a fundamental understanding of the nucleation of its different metastable phases is still lacking. Here we…
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…
Morphological evolution via surface diffusion learned by convolutional, recurrent neural networks: extrapolation and prediction uncertainty
Daniele Lanzoni, Marco Albani, Roberto Bergamaschini +1
We use a Convolutional Recurrent Neural Network approach to learn morphological evolution driven by surface diffusion. To this aim we first produce a training set using phase field…
Doubling the mobility of InAs/InGaAs selective area grown nanowires
Daria V. Beznasyuk, Sara Martí-Sánchez, Jung-Hyun Kang +12
Selective area growth (SAG) of nanowires and networks promise a route toward scalable electronics, photonics and quantum devices based on III-V semiconductor materials. The potenti…
Reentrant behavior of the density vs temperature of indium islands on GaAs(111)A
Artur Tuktamyshev, Alexey Fedorov, Sergio Bietti +4
We show that the density of indium islands on GaAs(111)A substrates have a non-monotonic, reentrant behavior as a function of the indium deposition temperature. The expected increa…
Kinetic Control of Morphology and Composition in Ge/GeSn Core/Shell Nanowires
Simone Assali, Roberto Bergamaschini, Emilio Scalise +8
The growth of Sn-rich group-IV semiconductors at the nanoscale provides new paths for understanding the fundamental properties of metastable GeSn alloys. Here, we demonstrate the e…
Strain engineering in Ge/GeSn core/shell nanowires
Simone Assali, Marco Albani, Roberto Bergamaschini +6
Strain engineering in Sn-rich group IV semiconductors is a key enabling factor to exploit the direct band gap at mid-infrared wavelengths. Here, we investigate the effect of strain…
Optically reconfigurable polarized emission in Germanium
Sebastiano De Cesari, Roberto Bergamaschini, Elisa Vitiello +2
Light polarization can conveniently encode information. Yet, the ability to steer polarized optical fields is notably demanding but crucial to develop practical methods for data en…