arXiv subjects

Francesco Montalenti

Publications and source records attributed to Francesco Montalenti.

At least 19 recordsLinked to original sources

arXiv2026

Growth and characterization of planar hexagonal Ge on CdS

Hexagonal group-IV semiconductors have attracted increasing interest owing to their unconventional electronic and optical properties compared to the cubic diamond phase. However, the stabilization of these metastable allotropes in planar heterostructures remains a major challenge. In this work, we demonstrate the epitaxial growth of planar hexagonal germanium on non-basal $m$-plane CdS substrates by low-energy plasma-enhanced chemical vapor deposition. The role of growth temperature in the formation and stabilization of the hexagonal phase is investigated. X-ray diffraction, scanning transmission electron microscopy, and polarization-resolved Raman spectroscopy reveal the formation of epitaxial hexagonal germanium with the expected crystal symmetry. In particular, the Raman response exhibits the characteristic polarization selection rules of the E$_\text{2g}$ phonon mode of hexagonal Ge. Conversely, photoluminescence spectroscopy does not reveal any Ge-related emission feature. Combined transmission electron microscopy observations and atomistic modeling show that strain relaxation is governed by a limited set of dislocation mechanisms, which efficiently relieve most of the mismatch strain within a few nanometers from the interface and can involve localized cubic stacking insertions. At greater distances from the interface, the progressive loss of hexagonal order is increasingly dominated by stacking-fault disorder, particularly I3-type defects. These results establish CdS as a promising template for the planar stabilization of hexagonal Ge and provide insight into the defect mechanisms governing strain relaxation in metastable group-IV heterostructures.

cond-mat.mtrl-sci↗

arXiv2026

Neural surrogates for crystal growth dynamics with variable supersaturation: explicit vs. implicit conditioning

Simulations of crystal growth are performed by using Convolutional Recurrent Neural Network surrogate models, trained on a dataset of time sequences computed by numerical integration of Allen-Cahn dynamics including faceting via kinetic anisotropy. Two network architectures are developed to take into account the effects of a variable supersaturation value. The first infers it implicitly by processing an input mini-sequence of a few evolution frames and then returns a consistent continuation of the evolution. The second takes the supersaturation parameter as an explicit input along with a single initial frame and predicts the entire sequence. The two models are systematically tested to establish strengths and weaknesses, comparing the prediction performance for models trained on datasets of different size and, in the first architecture, different lengths of input mini-sequence. The analysis of point-wise and mean absolute errors shows how the explicit parameter conditioning guarantees the best results, reproducing with high-fidelity the ground-truth profiles. Comparable results are achievable by the mini-sequence approach only when using larger training datasets. The trained models show strong conditioning by the supersaturation parameter, consistently reproducing its overall impact on growth rates as well as its local effect on the faceted morphology. Moreover, they are perfectly scalable even on 256 times larger domains and can be successfully extended to more than 10 times longer sequences with limited error accumulation. The analysis highlights the potential and limits of these approaches in view of their general exploitation for crystal growth simulations.

cond-mat.mtrl-sci↗

arXiv2026

Resolving the Metastable Si-XIII Structure through Convergent Theory and Experiment

Silicon is the undisputed cornerstone of modern technology, with applications ranging from micro- and opto-electronics to quantum technologies. Recently, the exploration of its allotropes has emerged as a pivotal frontier for engineering materials with tailored optical and electronic functionalities. High-pressure experiments have revealed several metastable silicon phases, among which is Si-XIII. First observed more than 20 years ago, this phase has remained structurally unidentified, representing a significant gap in our understanding of elemental silicon allotropy. In this work, a convergent methodology is employed combining advanced theoretical modeling with experimental characterization to finally resolve the long-standing structural assignment of Si-XIII. Guided by careful experimental observations, a structural model validated through first-principles optimization and systematically tested against multiple experimental signatures is constructed. All the fingerprints of this phase are rationalized by our proposed crystal structure: interplanar spacings, Raman frequencies, thermodynamic stability, and kinetic pathways. These findings provide a crucial missing piece in the high-pressure phase diagram of silicon and demonstrate the power of integrating computational predictions with experimental validation to resolve complex structural problems in materials science.

cond-mat.mtrl-sci↗

arXiv2026

Electronic States, Spin-Orbit Coupling and Magnetism in Germanium 60° Dislocations

Defects in semiconductors have recently attracted renewed interest owing to their potential in novel quantum applications. Here we investigate the electronic and magnetic properties induced by 60° dislocations in Ge. Using large-scale DFT calculations, we determine the band structure for both the shuffle and glide sets in their lowest-energy configurations. We also perform charged-defect calculations to aid in the interpretation of complex photoluminescence spectra observed in epitaxial Ge layers. The band structure for the shuffle set reveals defect-induced dispersive bands localized within the band gap near the Γ point, whereas for the glide set, we observe strong overlap with the conduction band. Defect-induced band splitting evident away from Γ reveals Rashba-Dresselhaus spin-orbit coupling, an effect previously reported only for screw dislocations. Remarkably, we find evidence that specific dislocation arrangements can stabilize antiferromagnetic ordering with sizable local magnetic moments and considerable exchange splitting between opposite spin states. These results uncover rich physics in Ge dislocations through the combination of spin-orbit coupling and magnetic ordering, potentially enabling novel defect-based functionalities in Ge devices.

cond-mat.mtrl-sci↗

arXiv2026

Cross-hatch strain effects on SiGe quantum dots for qubit variability estimation

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 strain inhomogeneity propagates into heterostructure overgrowth. To quantify strain inhomogeneity's influence on interface structure and qubit properties, we measure strained-silicon (s-Si)/Si$_{0.7}$Ge$_{0.3}$ heterostructures on 25 wafers processed via standard commercial chemical vapor deposition. Spatially-aligned images of strain (Raman microscopy) and interface structure (atomic force microscopy and cross-sectional scanning transmission electron microscopy) reveal strain-roughness interplay. A strain-driven surface diffusion model predicts the roughness and its temperature dependence. Measured strains suggest spurious double-dot qubit detunings of 0.1 meV over 100 nm distances may result. Modeling shows that interface roughness (atomic steps), when convolved with alloy disorder, only modestly reduces valley splitting (70$\pm$13 vs. 77$\pm$14 $μ$eV on average). Our findings point to thicker VS buffer layers beneath heterostructures and lower-temperature growth (T $\le$ 700 $^{\circ}$C) to limit roughening.

cond-mat.mtrl-sci↗

arXiv2025

Unified machine-learning framework for property prediction and time-evolution simulation of strained alloy microstructure

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 approach allows for the simultaneous extraction of elastic parameters from a short trajectory and for the prediction of further microstructure evolution under their influence. This is demonstrated by focusing on spinodal decomposition in the presence of a lattice mismatch eta, and by carrying out an extensive comparison between the ground-truth evolution supplied by phase field simulations and the predictions of suitable convolutional recurrent neural network architectures. The two tasks may then be performed subsequently into a cascade framework. Under a wide spectrum of misfit conditions, the here-presented cascade model accurately predicts eta and the full corresponding microstructure evolution, also when approaching critical conditions for spinodal decomposition. Scalability to larger computational domain sizes and mild extrapolation errors in time (for time sequences five times longer than the sampled ones during training) are demonstrated. The proposed framework is general and can be applied beyond the specific, prototypical system considered here as an example. Intriguingly, experimental videos could be used to infer unknown external parameters, prior to simulating further temporal evolution.

cond-mat.mtrl-sci↗

arXiv2025

Learning Kinetic Monte Carlo stochastic dynamics with Deep Generative Adversarial Networks

We show that Generative Adversarial Networks (GANs) may be fruitfully exploited to learn stochastic dynamics, surrogating traditional models while capturing thermal fluctuations. Specifically, we showcase the application to a two-dimensional, many-particle system, focusing on surface-step fluctuations and on the related time-dependent roughness. After the construction of a dataset based on Kinetic Monte Carlo simulations, a conditional GAN is trained to propagate stochastically the state of the system in time, allowing the generation of new sequences with a reduced computational cost. Modifications with respect to standard GANs, which facilitate convergence and increase accuracy, are discussed. The trained network is demonstrated to quantitatively reproduce equilibrium and kinetic properties, including scaling laws, with deviations of a few percent from the exact value. Extrapolation limits and future perspectives are critically discussed.

cond-mat.stat-mech↗

arXiv2024

Extreme time extrapolation capabilities and thermodynamic consistency of physics-inspired Neural Networks for the 3D microstructure evolution of materials via Cahn-Hilliard flow

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 equation. A specialized, physics-inspired architecture is proven to provide close accordance between the predicted evolutions and the ground truth ones obtained via conventional integration schemes. The method can accurately reproduce the evolution of microstructures not represented in the training set at a fraction of the computational costs. Extremely long-time extrapolation capabilities are achieved, up to reaching the theoretically expected equilibrium state of the system, consisting of a layered, phase-separated morphology, despite the training set containing only relatively-short, initial phases of the evolution. Quantitative accordance with the decay rate of the Free energy is also demonstrated up to the late coarsening stages, proving that this class of Machine Learning approaches can become a new and powerful tool for the long timescale and high throughput simulation of materials, while retaining thermodynamic consistency and high-accuracy.

cond-mat.mes-hall↗

arXiv2024

Unraveling the Atomic-Scale Pathways Driving Pressure-Induced Phase Transitions in Silicon

Silicon exhibits several metastable allotropes which recently attracted attention in the quest for materials with superior (e.g. optical) properties, compatible with Si technology. In this work we shed light on the atomic-scale mechanisms leading to phase transformations in Si under pressure. To do so, we synergically exploit different state-of-the-art approaches. In particular, we use the advanced GAP interatomic potential both in NPT molecular dynamics simulations and in solid-state nudged elastic band calculations, validating our predictions with ab initio DFT calculations. We provide a link between evidence reported in experimental nanoindentation literature and simulation results. Particular attention is dedicated to the investigation of atomistic transition paths allowing for the transformation between BC8/R8 phases to the hd one under pure annealing. In this case we show a direct simulation of the local nucleation of the hexagonal phase in a BC8/R8 matrix and its corresponding atomic-scale mechanism extracted by the use of SS-NEB. We extend our study investigating the effect of pressure on the nucleation barrier, providing an argument for explaining the heterogeneous nucleation of the hd phase observed in experiments reported in the literature.

cond-mat.mtrl-sci↗

arXiv2024

Polytypic Quantum Wells in Si and Ge: Impact of 2D Hexagonal Inclusions on Electronic Band Structure

Crystal defects, traditionally viewed as detrimental, are now being explored for quantum technology applications. This study focuses on stacking faults in silicon and germanium, forming hexagonal inclusions within the cubic crystal and creating quantum wells that modify electronic properties. By modeling defective structures with varying hexagonal layer counts, we calculated formation energies and electronic band structures. Our results show that hexagonal inclusions in Si and Ge exhibit a direct band gap, changing with inclusion thickness, effectively functioning as quantum wells. We find that Ge inclusions have a direct band gap and form Type-I quantum wells. This research highlights the potential of manipulating extended defects to engineer the optoelectronic properties of Si and Ge, offering new pathways for advanced electronic and photonic device applications.

cond-mat.mtrl-sci↗

arXiv2024

Quantitative analysis of the prediction performance of a Convolutional Neural Network evaluating the surface elastic energy of a strained film

A Deep Learning approach is devised to estimate the elastic energy density $ρ$ at the free surface of an undulated stressed film. About 190000 arbitrary surface profiles h(x) are randomly generated by Perlin noise and paired with the corresponding elastic energy density profiles $ρ(x)$, computed by a semi-analytical Green's function approximation, suitable for small-slope morphologies. The resulting dataset and smaller subsets of it are used for the training of a Fully Convolutional Neural Network. The trained models are shown to return quantitative predictions of $ρ$, not only in terms of convergence of the loss function during training, but also in validation and testing, with better results in the case of the larger dataset. Extensive tests are performed to assess the generalization capability of the Neural Network model when applied to profiles with localized features or assigned geometries not included in the original dataset. Moreover, its possible exploitation on domain sizes beyond the one used in the training is also analyzed in-depth. The conditions providing a one-to-one reproduction of the ground-truth $ρ(x)$ profiles computed by the Green's approximation are highlighted along with critical cases. The accuracy and robustness of the deep-learned $ρ(x)$ are further demonstrated in the time-integration of surface evolution problems described by simple partial differential equations of evaporation/condensation and surface diffusion.

physics.comp-ph↗

arXiv2023

Accurate generation of stochastic dynamics based on multi-model Generative Adversarial Networks

Generative Adversarial Networks (GANs) have shown immense potential in fields such as text and image generation. Only very recently attempts to exploit GANs to statistical-mechanics models have been reported. Here we quantitatively test this approach by applying it to a prototypical stochastic process on a lattice. By suitably adding noise to the original data we succeed in bringing both the Generator and the Discriminator loss functions close to their ideal value. Importantly, the discreteness of the model is retained despite the noise. As typical for adversarial approaches, oscillations around the convergence limit persist also at large epochs. This undermines model selection and the quality of the generated trajectories. We demonstrate that a simple multi-model procedure where stochastic trajectories are advanced at each step upon randomly selecting a Generator leads to a remarkable increase in accuracy. This is illustrated by quantitative analysis of both the predicted equilibrium probability distribution and of the escape-time distribution. Based on the reported findings, we believe that GANs are a promising tool to tackle complex statistical dynamics by machine learning techniques

cond-mat.stat-mech↗

arXiv2022

Morphological evolution via surface diffusion learned by convolutional, recurrent neural networks: extrapolation and prediction uncertainty

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 simulations. Intentionally, we insert in such a set only relatively simple, isolated shapes. After proper data augmentation, training and validation, the model is shown to correctly predict also the evolution of previously unobserved morphologies and to have learned the correct scaling of the evolution time with size. Importantly, we quantify prediction uncertainties based on a bootstrap-aggregation procedure. The latter proved to be fundamental in pointing out high uncertainties when applying the model to more complex initial conditions (e.g. leading to splitting of high aspect-ratio individual structures). Automatic smart-augmentation of the training set and design of a hybrid simulation method are discussed.

physics.comp-ph↗

arXiv2020

Reentrant behavior of the density vs temperature of indium islands on GaAs(111)A

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 increase in the density with decreasing temperature, indeed, is observed only down to 160 °C, where the indium islands undertake the expected liquid-to-solid phase transition. Further decreasing the temperature causes a sizeable reduction of the island density. An additional, reentrant increasing behavior is observed below 80 °C. We attribute the above complex behavior to the liquid-solid phase transition and to the complex island-island interaction which takes place between crystalline islands in the presence of strain. Indium solid islands grown at temperatures below 160 °C have a face-centered cubic crystal structure.

cond-mat.mtrl-sci↗

arXiv2020

Origin and nature of killer defects in 3C-SiC for power electronic applications by a multiscale atomistic approach

3C-SiC epitaxially grown on Si displays a large wealth of extended defects. In particular, single, double and triple stacking faults (SFs) are observed in several experiments to coexist. Overabundance of defects has so far limited the exploitation of 3C-SiC/Si for power electronics, in spite of its several ideal properties (mainly in terms of wide gap, high breakdown fields and thermal properties) and the possibility of a direct integration in the Si technology. Here we use a multiscale approach, based on both classical molecular dynamics (MD) simulations and first-principle calculations, to investigate in-depth the origin, nature and properties of most common 3C-SiC/Si(001) extended defects. Our MD simulations reveal a natural path for the formation of partial dislocation complexes terminating both double and triple SF's. MD results are used as input for superior DFT calculations, allowing us to better determine the core structure and to investigate electronic properties. It turns out that the partial dislocation complexes terminating double and triple SFs are responsible for the introduction of electronic states significantly filling the gap. On the other hand, individual partial dislocations terminating single SFs only induce states very close to the gap edge. We conclude that partial dislocation complexes, in particular the most abundant triple ones, are killer defects in terms of favoring leakage currents. Suggestions coming from theory/simulations for devising a strategy to lower their occurrence are discussed.

cond-mat.mtrl-sci↗

arXiv2020

Kinetic Control of Morphology and Composition in Ge/GeSn Core/Shell Nanowires

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 effect of the growth conditions on the morphology and composition of Ge/GeSn core/shell nanowires by correlating the experimental observations with a theoretical interpretation based on a multi-scale approach. We show that the cross-sectional morphology of Ge/GeSn core/shell nanowires changes from hexagonal to dodecagonal upon increasing the supply of the Sn precursor. This transformation strongly influences the Sn distribution as a higher Sn content is measured under the {112} growth front. Ab-initio DFT calculations provide an atomic-scale explanation by showing that Sn incorporation is favored at the {112} surfaces, where the Ge bonds are tensile-strained. A phase-field continuum model was developed to reproduce the morphological transformation and the Sn distribution within the wire, shedding light on the complex growth mechanism and unveiling the relation between segregation and faceting. The tunability of the photoluminescence emission with the change in composition and morphology of the GeSn shell highlights the potential of the core/shell nanowire system for opto-electronic devices operating at mid-infrared wavelengths.

cond-mat.mtrl-sci↗

arXiv2019

Temperature-dependent stability of polytypes and stacking faults in SiC: reconciling theory and experiments

The relative stability of SiC polytypes, changing with temperature, has been considered a paradox for about thirty years, due to discrepancies between theory and experiments. Based on ab-initio calculations including van der Waals corrections, a temperature-dependent polytypic diagram consistent with the experimental observations is obtained. Results are easily interpreted based on the influence of the hexagonality on both cohesive energy and entropy. Temperature-dependent stability of stacking faults is also analyzed and found to be in agreement with experimental evidences. Our results suggest that lower temperatures during SiC crystal deposition are advantageous in order to reduce ubiquitous stacking faults in SiC-based power devices.

cond-mat.mtrl-sci↗

arXiv2012

Monolithic growth of ultra-thin Ge nanowires on Si(001)

Self-assembled Ge wires with a height of only 3 unit cells and a length of up to 2 micrometers were grown on Si(001) by means of a catalyst-free method based on molecular beam epitaxy. The wires grow horizontally along either the [100] or the [010] direction. On atomically flat surfaces, they exhibit a highly uniform, triangular cross section. A simple thermodynamic model accounts for the existence of a preferential base width for longitudinal expansion, in quantitative agreement with the experimental findings. Despite the absence of intentional doping, first transistor-type devices made from single wires show low-resistive electrical contacts and single hole transport at sub-Kelvin temperatures. In view of their exceptionally small and self-defined cross section, these Ge wires hold promise for the realization of hole systems with exotic properties and provide a new development route for silicon-based nanoelectronics.

cond-mat.mtrl-sci↗