arXiv subjects

Silvana Botti

Publications and source records attributed to Silvana Botti.

At least 19 recordsLinked to original sources

arXiv2026

Fingerprints of Excitonic Collective Modes in the Two-Dimensional Electron Gas

The two-dimensional homogeneous electron gas (2D HEG) is a prototype system of fundamental interest that can be realized experimentally over a wide range of densities. Here, we investigate its collective charge excitations at low densities, using time-dependent density functional theory. We demonstrate that, beyond traditional plasmons, new collective excitonic modes emerge for a Wigner-Seitz radius larger than $r_{\mathrm s} \approx 1$. These excitonic modes leave characteristic fingerprints in experimentally accessible quantities, namely, asymmetric peak structures in the loss function and very strong Friedel-like oscillations in the static linear density response that increase when the system approaches a regime of instability. Indeed, at low enough densities the collective modes cross the zero energy axis, indicating an instability of the paramagnetic 2D HEG towards the formation of a charge-density-wave phase with excitonic origin. These findings provide valuable insights for the experimental detection of excitonic collective modes in tunable 2D electron systems and contribute to the fundamental understanding of many-body effects in low-density electron gases.

cond-mat.mes-hall↗

arXiv2026

Unravelling the Role of Stacking Disorder on the Optoelectronic Properties of Zn3P2

Zinc phosphide (Zn3P2) is a promising photovoltaic absorber for thin-film and flexible solar cells due to its earth-abundant composition and favourable optoelectronic properties. Recent advances in epitaxy have enabled the growth of high-quality Zn3P2 thin films despite the challenges posed by its incompatible lattice parameter and thermal expansion coefficient. However, Zn3P2 remains prone to intrinsic extended defects, such as rotated domains, that can limit device performance. Here, using (scanning) transmission electron microscopy, we identify a previously unreported class of extended defects that appear as planar faults described by displacement vectors lying in the (001) plane. Within a pseudo-cubic description of Zn3P2, we establish a direct correspondence between planar faults and rotated domains, showing that both arise from the flexible ordering of vacant sites in the Zn sublattice. First-principles calculations reveal an extremely low planar-defect formation energy of 2.5 mJ m-2, demonstrating that these defects form at essentially negligible energetic cost, in excellent agreement with their high experimentally observed occurrence. Additional density functional theory (DFT) calculations show that intrinsic planar defects neither introduce mid-gap electronic states nor significantly perturb the local electrostatic potential, indicating that they are electronically benign. Instead, we propose that planar defects indirectly degrade device performance by acting as preferential segregation sites for optically active point defects.

cond-mat.mtrl-sci↗

arXiv2026

Cathodoluminescence Analysis of Defects and Grain Boundaries in Zn3P2 Thin Films Grown on Graphene by MOVPE and MBE

Zn3P2 is a promising earth-abundant absorber for thin-film photovoltaics, yet its development is hindered by the lack of lattice-matched substrates, its incompatible thermal expansion coefficient, and a complex defect landscape. Here, we demonstrate the quasi-van der Waals epitaxy of Zn3P2 on graphene by metal-organic vapour phase epitaxy (MOVPE) and directly link the density of antiphase boundaries to optical emission modulation using correlative electron microscopy and cathodoluminescence (CL). Moreover, it is observed through CL that grain boundaries act as non-radiative sinks for excited charge carriers. The effect extends several micrometres into the grains, making grain boundaries detrimental to the applicability of Zn3P2 in read devices. Further comparison with molecular beam epitaxy grown films reveals the suppression of strain-related sub-bandgap emission in MOVPE-grown material. Overall, quasi-van der Waals epitaxy of Zn3P2 by MOVPE resulted in larger grains and improved material quality. In addition, these results directly link extended defects to recombination pathways in Zn3P2 and highlight grain-size control as a key strategy for improving earth-abundant photovoltaic absorbers.

cond-mat.mtrl-sci↗

arXiv2026

Intrinsic Point Defects and Frenkel Pair Formation in Photovoltaic Absorber Zn$_3$P$_2$: Regulating $p$-type Conductivity through Growth and Annealing Conditions

This study investigates the ground-state energetics and thermodynamics of intrinsic point defects in zinc phosphide Zn$_3$P$_2$ using \emph{ab initio} density functional theory combined with an extensive potential energy landscape search. Our analysis reveals that the defect chemistry is dominated by zinc vacancies $V_\mathrm{Zn}$ and zinc interstitials Zn$_i$, with equilibrium concentrations significantly surpassing those of other intrinsic species. Notably, we find that phosphorus interstitials P$_i$, previously suggested to be significant, possess high formation energies and likely exist only in negligible quantities. The characteristic $p$-type conductivity of undoped Zn$_3$P$_2$ is shown to be a direct consequence of zinc vacancies, which act as shallow acceptors and pull the Fermi level toward the valence band. Furthermore, we identify a positive binding energy between $V_\mathrm{Zn}$ and Zn$_i$, leading to the formation of electrically benign Frenkel pairs that partially compensate the intrinsic p-type conductivity. Our results suggest that achieving $n$-type conductivity is fundamentally limited by these thermodynamic constraints. We conclude that hole densities can be optimized through phosphorus-rich growth conditions and high-temperature annealing, and suggest that future photovoltaic strategies should prioritize interface engineering over bulk $n$-type doping.

cond-mat.mtrl-sci↗

arXiv2026

High-throughput study of electrical conductivity in ordered metals

We present a computational framework that integrates machine learning with high-throughput ab initio calculations to screen over 2.8 million compounds for metallic transport. We identify several intermetallic candidates with predicted high conductivities comparable to that of aluminum (36.59 x $10^6$ S/m). We perform full electron-phonon coupling calculations for the top-performing materials, yielding results in good agreement with available experimental data. Our analysis reveals that while the noble metals (Ag, Au, Cu) possess a conductivity that remains difficult to surpass due to their unique electronic structure and low scattering, compounds like LiBePt2 can achieve comparable performance by utilizing valence electrons from light elements to shift high-scattering d-states beneath the Fermi level. This study not only identifies novel high-performance conductors but also demonstrates the predictive power of combining statistical learning with detailed ab initio calculations.

cond-mat.mtrl-sci↗

arXiv2026

Functional and Density-Driven Errors in Density Functional Theory: Quantum Monte Carlo Benchmarks for Solids

We introduce a systematic analysis of density functional approximation errors in solids by separating functional-driven from density-driven contributions using quantum Monte Carlo densities of silicon, sodium chloride, and copper as reference. Typically, functional errors dominate, but we identify important exceptions where density-driven errors exceed functional errors by factors of 2-3, notably for SOGGA11 and {\tau}-HCTH in the semiconductor and the insulator. Material dependence is striking: 63% of functionals show error cancellation in silicon versus 18% in copper, and only five functionals surpass LDA accuracy for metallic copper even with exact densities. For silicon and sodium chloride, GILL or BECKE exchange combined with PBE, PW91, or P86 correlation achieves near-exact xc energies on QMC densities, while copper requires specialized functionals like PBEsol or PBELYP. High-quality densities consistently reduce density-driven errors across all systems. Historical analysis reveals that 1990s GGA functionals outperform many modern meta-GGAs, contradicting expectations of systematic improvement along Jacob's ladder. These results provide practical guidance for functional selection and highlight implications for machine learning potential development, where material-dependent error cancellation may compromise transferability.

cond-mat.mtrl-sci↗

arXiv2026

Optical selection rules in hexagonal Ge polytypes and their lifting by symmetry perturbation

Hexagonal germanium polytypes have emerged as promising direct-gap semiconductors for silicon-integrated optoelectronics, yet their optical properties remain largely unexplored beyond the well-studied 2H phase. We present a comprehensive theoretical study of optical properties of hexagonal 2H-, 4H-, and 6H-Ge polytypes through ab initio calculations of quasiparticle band structures, dipole transition matrix elements, and solution of the Bethe-Salpeter equation. While all three polytypes exhibit direct band gaps of increasing size from 2H to 6H, we reveal that the fundamental optical transition in 4H-Ge is parity-forbidden due to matching band parities at the valence and conduction band edges. This selection rule results in a radiative lifetime seven orders of magnitude longer than in 2H- and 6H-Ge, severely limiting light emission capabilities. To demonstrate that the selection rule can be lifted, we introduce controlled symmetry perturbations by substituting single Ge atoms with Si in each unit cell, breaking the crystal symmetry. This perturbation increases the optical matrix elements by up to two orders of magnitude and reduces radiative lifetimes for all perturbed polytypes. We also compute absorption coefficients and frequency-dependent dielectric tensors for both light polarizations, including excitonic effects up to 5 eV, providing complete optical characterization of ideal and symmetry-perturbed hexagonal Ge systems relevant for optoelectronic applications.

cond-mat.mtrl-sci↗

arXiv2025

AI-Driven Expansion and Application of the Alexandria Database

We present a novel multi-stage workflow for computational materials discovery that achieves a 99% success rate in identifying compounds within 100 meV/atom of thermodynamic stability, with a threefold improvement over previous approaches. By combining the Matra-Genoa generative model, Orb-v2 universal machine learning interatomic potential, and ALIGNN graph neural network for energy prediction, we generated 119 million candidate structures and added 1.3 million DFT-validated compounds to the ALEXANDRIA database, including 74 thousand new stable materials. The expanded ALEXANDRIA database now contains 5.8 million structures with 175 thousand compounds on the convex hull. Predicted structural disorder rates (37-43%) match experimental databases, unlike other recent AI-generated datasets. Analysis reveals fundamental patterns in space group distributions, coordination environments, and phase stability networks, including sub-linear scaling of convex hull connectivity. We release the complete dataset, including sAlex25 with 14 million out-of-equilibrium structures containing forces and stresses for training universal force fields. We demonstrate that fine-tuning a GRACE model on this data improves benchmark accuracy. All data, models, and workflows are freely available under Creative Commons licenses.

cond-mat.mtrl-sci↗

arXiv2025

Unconventional cross sections in zinc phosphide nanowires grown using exclusively earth-abundant components

To enable lightweight and flexible solar cell applications it is imperative to develop direct bandgap absorber materials. Moreover, to enhance the potential sustainability impact of the technologies there is a drive to base the devices on earth-abundant and readily available elements. Herein, we report on the epitaxial growth of Zn3P2 nanowires using exclusively earth-abundant components, using Sn as the nanowire catalyst and Si (111) as the substrate. We observe that the nanowires exhibit a triangular cross section at lower temperatures, a pseudo-pentagonal cross section at intermediate temperatures, and a hexagonal cross section in a twin plane superlattice configuration at high temperatures and high V/II ratios. At low temperatures, the surface facets are constricted into a metastable configuration, yielding the triangular morphology due to the symmetry of the substrate, while intermediate temperatures facilitate the formation of a pseudo-pentagonal morphology with lower surface to volume ratio. The twin plane superlattice structure can only be observed at conditions that facilitate the incorporation of Sn into Zn3P2, which is needed to form heterotwins in the tetragonal structure, namely at high temperatures and high phosphine partial pressures. These findings show a clear pathway to use Zn3P2 nanowires in sustainable solar energy harvesting using exclusively earth-abundant components, as well as opening up a novel route of fabricating quantum wells inside nanowires using heterotwins.

cond-mat.mtrl-sci↗

arXiv2025

Generative AI for Crystal Structures: A Review

As in many other fields, the rapid rise of generative artificial intelligence is reshaping materials discovery by offering new ways to propose crystal structures and, in some cases, even predict desired properties. This review provides a comprehensive survey of recent advancements in generative models specifically for inorganic crystalline materials. We begin by introducing the fundamentals of generative modeling and invertible material descriptors. We then propose a taxonomy based on architecture, representation, conditioning, and materials domain to categorize the diverse range of current generative AI models. We discuss data sources and address challenges related to performance metrics, emphasizing the need for standardized benchmarks. Specific examples and applications of novel generated structures are presented. Finally, we examine current limitations and future directions in this rapidly evolving field, highlighting its potential to accelerate the discovery of new inorganic materials.

cond-mat.mtrl-sci↗

arXiv2025

MACE4IRmol: An uncertainty-aware foundation model for molecular infrared spectroscopy

Machine-learned interatomic potentials (MLIPs) have shown significant promise in predicting infrared spectra with high fidelity. However, the absence of general-purpose MLIPs that simultaneously span broad chemical diversity and provide reliable uncertainty estimates has limited their wider applicability. In this work, we introduce MACE4IRmol, an uncertainty-aware foundation model ensemble built on the MACE architecture. MACE4IRmol is trained on ~16 million molecular geometries and the corresponding density-functional theory (DFT) energies, forces, and dipole moments from the QCML dataset. The training data encompasses approximately 80 elements and a diverse set of molecules, including organic and inorganic compounds, and metal complexes. Importantly, MACE4IRmol is formulated as an ensemble of models to enable uncertainty quantification, which helps improve robustness in chemically diverse systems. Within this ensemble, separate models are trained with and without explicit dispersion corrections, allowing systematic assessment of van der Waals effects. In addition, MACE4IRmol delivers accurate predictions of energies, forces, dipole moments, and infrared spectra at a fraction of the computational cost of DFT, while enabling the explicit inclusion of nuclear quantum effects in infrared spectrum simulations. By combining generality, accuracy, efficiency, and uncertainty estimation, MACE4IRmol opens the door to rapid and reliable infrared spectra prediction for complex and diverse molecular systems.

physics.chem-ph↗

arXiv2025

Universal Machine Learning Potentials under Pressure

Universal machine learning interatomic potentials (uMLIPs) represent arguably the most successful application of machine learning to materials science, demonstrating remarkable performance across diverse applications. However, critical blind spots in their reliability persist. Here, we address one such significant gap by systematically investigating the accuracy of uMLIPs under extreme pressure conditions from 0 to 150 GPa. Our benchmark reveals that while these models excel at standard pressure, their predictive accuracy deteriorates considerably as pressure increases. This decline in performance originates from fundamental limitations in the training data rather than in algorithmic constraints. In fact, we show that through targeted fine-tuning on high-pressure configurations, the robustness of the models can be easily increased. These findings underscore the importance of identifying and addressing overlooked regimes in the development of the next generation of truly universal interatomic potentials.

cond-mat.mtrl-sci↗

arXiv2025

Universal Machine Learning Potential for Systems with Reduced Dimensionality

We present a benchmark designed to evaluate the predictive capabilities of universal machine learning interatomic potentials across systems of varying dimensionality. Specifically, our benchmark tests zero- (molecules, atomic clusters, etc.), one- (nanowires, nanoribbons, nanotubes, etc.), two- (atomic layers and slabs) and three-dimensional (bulk materials) compounds. The benchmark reveals that while all tested models demonstrate excellent performance for three-dimensional systems, accuracy degrades progressively for lower-dimensional structures. The best performing models for geometry optimization are orbital version 2, equiformerV2, and the equivariant Smooth Energy Network, with the equivariant Smooth Energy Network also providing the most accurate energies. Our results indicate that the best models yield, on average, errors in the atomic positions in the range of 0.01-0.02 angstrom and errors in the energy below 10~meV/atom across all dimensionalities. These results demonstrate that state-of-the-art universal machine learning interatomic potentials have reached sufficient accuracy to serve as direct replacements for density functional theory calculations, at a small fraction of the computational cost, in simulations spanning the full range from isolated atoms to bulk solids. More significantly, the best performing models already enable efficient simulations of complex systems containing subsystems of mixed dimensionality, opening new possibilities for modeling realistic materials and interfaces.

cond-mat.mtrl-sci↗

arXiv2025

Electronic structure reorganization in MPS3 via d-shell-selective alkali metal doping

Semiconducting two-dimensional (2D) antiferromagnetic (AFM) transition-metal thiophosphates (MPS3) offer promising opportunities for spintronic applications due to their highly tunable electronic properties. While alloying and intercalation have been shown to modulate ground states, the role of d-shell filling in governing these transitions remains insufficiently understood. Here, we investigate electron doping effects in MPS3 using angle-resolved photoemission spectroscopy (ARPES), X-ray photoelectron spectroscopy (XPS), and density functional theory (DFT+U). Lithium and cesium deposition are employed to induce doping across different MPS3 compounds. We identify two distinct doping mechanisms: in MnPS3, electrons are primarily donated to the P2S6 ligand clusters, with negligible Mn 2p core-level shifts and no major changes in the valence band. In contrast, FePS3, CoPS3, and NiPS3 exhibit clear reductions in transition-metal oxidation states, with a 1.0 eV reduction in spin-orbit splitting for Co upon doping. ARPES on CoPS3 reveals a 400 meV shift of Co-derived bands towards higher binding energies and new dispersive states up to 1 eV above the valence band maximum, indicating metallic behavior. These results establish a direct correlation between d-shell filling and doping response, highlighting alkali metal doping as a tunable route to tailor the electronic and magnetic properties of 2D AFM semiconductors for spintronic applications.

cond-mat.mtrl-sci↗

arXiv2025

Enhanced superconductivity in X4H15compounds via hole-doping at ambient pressure

This study presents a computational investigation of X4H15 compounds (where X represents a metal) as potential superconductors at ambient conditions or under pressure. Through systematic density functional theory calculations and electron-phonon coupling analysis, we demonstrate that electronic structure engineering via hole doping dramatically enhances the superconducting properties of these materials. While electron-doped compounds with X4+ cations (Ti, Zr, Hf, Th) exhibit modest transition temperatures of 1-9 K, hole-doped systems with X3+cations (Y, Tb, Dy, Ho,Er, Tm, Lu) show remarkably higher values of approximately 50 K at ambient pressure. Superconductivity in hole-doped compounds originates from stronger coupling between electrons and both cation and hydrogen phonon modes. Although pristine X3+4H15compounds are thermodynamically unstable, we propose a viable synthesis route via controlled hole doping of the charge-compensated YZr3H15 compound. Our calculations predict that even minimal concentrations of excess Y could induce high-temperature superconductivity while preserving structural integrity. This work reveals how strategic electronic structure modulation can optimize superconducting properties in hydride systems, establishing a promising pathway toward practical high-temperature conventional super-conductors at ambient pressure

cond-mat.supr-con↗

arXiv2025

Real-time simulations of laser-induced electron excitations in crystalline ZnO

We investigate non-equilibrium electron dynamics in crystalline ZnO induced by ultrashort, relatively intense, infrared laser pulses. Our focus is on understanding the mechanism that facilitates efficient conduction band population in ZnO to enable optically pumped lasing. We consider two different pulse frequencies (in the near-infrared and mid-infrared) for which experimental data are available, and we calculate the electronic response of a ZnO crystal for a wide range of pulse intensities. We apply and compare three complementary theoretical approaches: the analytical Keldysh model, the numerical solution of the semiconductor Bloch equations, and real-time time-dependent density functional theory. We conclude that time-dependent density functional theory is a valid \textit{ab initio} approach for predicting conduction band population, that offers an accurate enough description of static and transient optical properties of solids and provides physics insight into the intermediate excitation regime, where electronic excitations are determined by the interplay of intraband tunneling, a consequence of band bending, and interband multi-photon absorption.

cond-mat.mtrl-sci↗

arXiv2025

The Maximum $T_c$ of Conventional Superconductors at Ambient Pressure

The theoretical maximum critical temperature ($T_c$) for conventional superconductors at ambient pressure remains a fundamental question in condensed matter physics. Through analysis of electron-phonon calculations for over 20,000 metals, we critically examine this question. We find that while hydride metals can exhibit maximum phonon frequencies of more than 5000 K, the crucial logarithmic average frequency $\omega_\text{log}$ rarely exceeds 1800 K. Our data reveals an inherent trade-off between $\omega_\text{log}$ and the electron-phonon coupling constant $\lambda$, suggesting that the optimal Eliashberg function that maximizes $T_c$ is unphysical. Based on our calculations, we identify Li$_2$AgH$_6$ and its sibling Li$_2$AuH$_6$ as theoretical materials that likely approach the practical limit for conventional superconductivity at ambient pressure. Analysis of thermodynamic stability indicates that compounds with higher predicted $T_c$ values are increasingly unstable, making their synthesis challenging. While fundamental physical laws do not strictly limit $T_c$ to low-temperatures, our analysis suggests that achieving room-temperature conventional superconductivity at ambient pressure is extremely unlikely.

cond-mat.supr-con↗

arXiv2024

Universal Machine Learning Interatomic Potentials are Ready for Phonons

There has been an ongoing race for the past several years to develop the best universal machinelearning interatomic potential. This progress has led to increasingly accurate models for predictingenergy, forces, and stresses, combining innovative architectures with big data. Here, we benchmarkthese models on their ability to predict harmonic phonon properties, which are critical for under-standing the vibrational and thermal behavior of materials. Using around 10 000 ab initio phononcalculations, we evaluate model performance across various phonon-related parameters to test theuniversal applicability of these models. The results reveal that some models achieve high accuracyin predicting harmonic phonon properties. However, others still exhibit substantial inaccuracies,even if they excel in the prediction of the energy and the forces for materials close to dynamicalequilibrium. These findings highlight the importance of considering phonon-related properties inthe development of universal machine learning interatomic potentials.

cond-mat.mtrl-sci↗