arXiv subjects

Claudio Cazorla

Publications and source records attributed to Claudio Cazorla.

At least 19 recordsLinked to original sources

arXiv2026

Light-facilitated ferroelectric switching in wurtzite crystals

Wurtzite ferroelectrics combine large remanent polarization with full CMOS compatibility, positioning them as a leading platform for next-generation non-volatile memory. Their practical deployment, however, is hindered by an intrinsically large coercive field, rooted in the high energy barrier separating the polar wurtzite phase from the nonpolar hexagonal phase that mediates polarization switching. Here, using first-principles calculations, we propose an alternative, field-free strategy for lowering this barrier: above-bandgap electronic photoexcitation. Taking LaN as a representative wurtzite ferroelectric, we show that light induced carriers dramatically reduce the energy difference between the hexagonal intermediate phase and the wurtzite ground state, sharply reducing the energy barrier to ferroelectric switching. This effect originates from a photoinduced partial metallization of the polar phase, which screens the dipole-dipole interactions that stabilize ferroelectric order and thereby favors the competing nonpolar structure. The robustness of this mechanism is further confirmed for the rocksalt polymorph. Our results establish light as a powerful, non-invasive route to controlling ferroelectric switching in wurtzites, opening a path toward faster, lower-voltage, and more energy-efficient non-volatile memory technologies.

cond-mat.mtrl-sci↗

arXiv2026

Machine Learning-Accelerated Band-Edge Engineering of Pnictogen Chalcohalide Solid Solutions for Solar Energy Technologies

Pnictogen chalcohalide (MChX; M=Bi,Sb; Ch=S,Se; X=I,Br) solid solutions combine earth-abundant constituents, tunable band gaps ($1.2$-$2.1$ eV), and strong optical absorption, making them attractive for solar energy conversion. Yet their vast compositional space has so far prevented a systematic assessment of how band-edge positions vary with stoichiometry and surface termination. Here, we combine first-principles density functional theory with machine learning to predict the valence and conduction band-edge positions of $\mathrm{Bi}_x\mathrm{Sb}_{1-x}\mathrm{S}_y\mathrm{Se}_{1-y}\mathrm{I}_z\mathrm{Br}_{1-z}$ solid solutions across their full compositional range on the two most stable surfaces, (010) and (011). We find that the valence-band maximum and conduction-band minimum can be tuned by more than $1$ eV through composition alone, and shift by up to $0.6$ eV between the two surface terminations for a same composition despite their nearly degenerate formation energies, establishing facet selection as a design parameter on par with chemical substitution. Guided by these results, we identify specific compositions capable of driving hydrogen, ammonia, methane, hydrogen peroxide, and oxygen (photo)electrochemical half-reactions, and show that several electron- and hole-transport contact materials commonly used in photovoltaic devices align with MChX solid solutions only as hole-selective contacts.

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arXiv2026

Thermodynamics-Informed Machine Learning for Energy Materials Discovery

Machine learning (ML) is transforming materials discovery by enabling rapid prediction of properties that previously required computationally expensive first-principles calculations. Yet most current ML models remain fundamentally limited to zero-temperature descriptions, learning static lattice energies while neglecting the thermodynamic effects that govern materials behaviour at finite temperature. Because phase stability, functional response, and performance are governed by free-energy landscapes rather than static energies alone, this limitation represents a major barrier to predictive materials design under realistic operating conditions. In this Perspective, we argue that developing thermodynamics-informed ML constitutes one of the most important and least explored frontiers in materials discovery. We examine the fundamental shortcomings of energy-based models, highlighting the essential roles of entropy and anharmonicity in determining free energies and materials functionality. We review emerging strategies, including machine-learned interatomic potentials and hybrid ML-statistical mechanics frameworks, while identifying key challenges related to data availability, transferability, and thermodynamic consistency. Building on these advances, we outline a roadmap for thermodynamics-informed ML centred on direct free-energy learning, entropy-aware representations, and adaptive sampling across temperature. We highlight the transformative opportunities this paradigm offers for energy materials and argue that the next generation of ML models must move beyond static energy predictions towards a thermodynamic description of materials behaviour under realistic operating conditions.

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arXiv2026

Carrier Localization in Pnictogen-Based Chalcohalides from Defect-Bound Hot Polarons

Pnictogen-based solar absorbers have gained prominence as promising nontoxic and stable alternatives to lead-halide perovskites (LHPs), but are severely limited by carrier localization, preventing their performance from approaching those of LHPs. Recent efforts have uncovered routes to overcome carrier localization, but these early efforts only considered intrinsic factors. Herein, we push beyond these limited early efforts, examining the role of defects, not only on cold carriers but also hot carriers. Focusing on the structurally one-dimensional pnictogen chalcohalide BiSBr, we find that whilst this material intrinsically does not exhibit carrier localization, vacancies introduced during synthesis or post-treatment lead to pronounced extrinsic self-trapping via the formation of defect-bound hot polarons-excited charge-carriers strongly coupled to local defect-induced vibrational modes. These above-gap defect states divert hot carriers from cooling to the band edge, thus depleting the mobile carrier population. Our findings establish the key role of defect-bound hot polarons in mediating extrinsic localization and offer new mechanistic insights into the interplay between defects, lattice coupling, and excited-state charge-carrier transport, which are critical to designing efficient perovskite-inspired solar absorbers.

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arXiv2026

Topographic patterning in perovskite oxide membranes for local control of strain, nanomechanics and electronic structure

Single-crystalline perovskite oxide membranes provide a powerful platform to access physical properties that are inaccessible in bulk crystals and substrate-clamped thin films. Within this context, the deliberate fabrication of tailored corrugations provides a reliable mean to impose local curvature enabling deterministic modulation of functional properties. Here, we demonstrate controlled topographic patterning in (00l)-oriented La$_{0.7}$Sr$_{0.3}$MnO$_3$ (LSMO) membranes with thicknesses ranging from 4 to 100 nm where they spontaneously form sinusoidal wrinkles with thickness-dependent periodicity and amplitude. The wrinkle morphology directly modulates membrane stiffness and generates exceptionally large local strains exceeding 5\% with strain gradients approaching $\sim$ 2.5 x 10$^{7}$ m$^{-1}$ in the thinnest membranes. These extreme deformations suppress antiferrodistortive octahedral rotations and stabilize polar distortions, evidencing a curvature-driven symmetry transformation. The surface potential variation reinforces the formation of wrinkled-induced polar patterns being strongly modulated with thickness. The variation of Mn oxidation state from $\sim$ 3.2+ to $\sim$ 2.85+ provides a direct chemical signature of a thickness-controlled electronic transition. These results demonstrate that corrugation-induced strain gradients in oxide membranes with different thicknesses can drive coupled structural, nanomechanical and electronic transformations, offering a singular route to engineer their functional states for next-generation electronic devices.

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arXiv2026

Theory-Guided Discovery of Pressure-Induced Transitions in Fast-Ion Conductor BaSnF4

Fast-ion conductors such as BaSnF4 are of significant interest for next-generation solid-state battery technologies due to their high ionic conductivity and chemical stability. However, the behaviour of these materials under extreme conditions remains poorly understood, despite the relevance of pressure-induced modifications for tuning functional properties. In this study, we combine density functional theory (DFT) calculations with high-pressure experiments to investigate the structural evolution of BaSnF4 up to 40 GPa. DFT predicts two pressure-induced phase transitions: from the ambient-pressure tetragonal P4/nmm phase to a monoclinic P21/m-I structure at 10 GPa, and subsequently to a denser monoclinic P21/m-II phase at 32 GPa. The first transition is experimentally confirmed via angle-dispersive X-ray diffraction, Raman spectroscopy, and electrical resistivity measurements, all performed at ambient temperature. The second transition is supported by distinct changes in high-pressure Raman modes and resistivity behaviour, consistent with a further structural reorganization. These findings not only clarify the high-pressure phase diagram of BaSnF4, but also shed light on the potential for pressure-tuned ionic transport in fluorostannate-based solid electrolytes.

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arXiv2026

Machine Learning Modeling of Temperature-Dependent Optoelectronic Properties of Anharmonic Solid Solutions

Leveraging strong optoelectronic responses to external stimuli, such as temperature and electric fields, is central to the development of advanced photonic technologies, including adaptive photodetectors and reconfigurable photovoltaic devices. However, only a limited number of semiconducting materials, typically characterized by strong electron-phonon coupling, are known to exhibit such pronounced responsiveness, and their equilibrium optoelectronic properties are often not optimally suited for targeted applications. Chemical engineering strategies, such as doping and solid-solution mixing, are therefore widely employed to fine-tune the electronic and optical properties of semiconductors. Predicting the impact of such modifications, however, remains highly challenging due to the intrinsic complexity of chemically disordered and anharmonic systems, as well as the computational limitations of conventional first-principles approaches. In this work, we introduce a new computational framework that combines ab initio electronic-structure methods with machine-learning techniques to achieve first-principles precision in the prediction of optoelectronic properties of anharmonic solid solutions at finite temperature. We apply this approach to silver chalco-halide solid solutions, an emergent class of optoelectronic materials that have been experimentally shown to exhibit large band-gap tunability and strong responses to thermal excitations. Our results provide quantitative insight into the interplay between chemical disorder, lattice dynamics, and electronic structure in these materials. More broadly, this study establishes a general strategy for the accurate modeling of optoelectronic functionality in chemically disordered semiconductors.

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arXiv2025

Defect-Limited Efficiency of Pnictogen Chalcohalide Solar Cells

Pnictogen chalcohalides (MChX) have recently emerged as promising nontoxic and environmentally friendly photovoltaic absorbers, combining strong light absorption coefficients with favorable low-temperature synthesis conditions. Despite these advantages and reported optimized morphologies, device efficiencies remain below 10%, far from their ideal radiative limit. To uncover the origin of these performance losses, we present a systematic and fully consistent first-principles investigation of the defect chemistry across the Bi-based chalcohalide family. Our results reveal a complex defect landscape dominated by chalcogen vacancies of low formation energy, which act as deep nonradiative recombination centers. Despite their moderate charge-carrier capture coefficients, the high equilibrium concentrations of these defects reduce the theoretical maximum efficiencies by 6% in BiSeI and by 10% in BiSeBr. In contrast, sulfur vacancies in BiSI and BiSBr are comparatively benign, presenting smaller capture coefficients due to weaker electron-phonon coupling. Interestingly, despite its huge nonradiative charge-carrier recombination rate, BiSeI presents the best conversion efficiency among all four compounds owing to its most suitable bandgap for outdoor photovoltaic applications. Our findings identify defect chemistry as a critical bottleneck in MChX solar cells and proposes chalcogen-rich synthesis conditions and targeted anion substitutions as effective strategies for mitigation of detrimental vacancies.

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arXiv2025

Why Physics Still Matters: Improving Machine Learning Prediction of Material Properties with Phonon-Informed Datasets

Machine learning (ML) methods have become powerful tools for predicting material properties with near first-principles accuracy and vastly reduced computational cost. However, the performance of ML models critically depends on the quality, size, and diversity of the training dataset. In materials science, this dependence is particularly important for learning from low-symmetry atomistic configurations that capture thermal excitations, structural defects, and chemical disorder, features that are ubiquitous in real materials but underrepresented in most datasets. The absence of systematic strategies for generating representative training data may therefore limit the predictive power of ML models in technologically critical fields such as energy conversion and photonics. In this work, we assess the effectiveness of graph neural network (GNN) models trained on two fundamentally different types of datasets: one composed of randomly generated atomic configurations and another constructed using physically informed sampling based on lattice vibrations. As a case study, we address the challenging task of predicting electronic and mechanical properties of a prototypical family of optoelectronic materials under realistic finite-temperature conditions. We find that the phonons-informed model consistently outperforms the randomly trained counterpart, despite relying on fewer data points. Explainability analyses further reveal that high-performing models assign greater weight to chemically meaningful bonds that control property variations, underscoring the importance of physically guided data generation. Overall, this work demonstrates that larger datasets do not necessarily yield better GNN predictive models and introduces a simple and general strategy for efficiently constructing high-quality training data in materials informatics.

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arXiv2025

Molecular ink-based synthesis of Bi(SzSe1-z)(IxBr1-x) solid solutions as tuneable materials for sustainable energy applications

Quasi-one-dimensional (Q-1D) van der Waals chalcohalides have emerged as promising materials for advanced energy applications, combining tunable optoelectronic properties and composed by earth-abundant and non-toxic elements. However, their widespread application remains hindered by challenges such as anisotropic crystal growth, composition control and lack of knowledge on optoelectronic properties. A deeper understanding of the intrinsic limitations of these materials, as well as viable defect mitigation strategies like the engineering of solid solutions, is critical. This work presents a low-temperature synthesis route based on molecular ink deposition enabling direct crystallization of tunable Bi(SzSe1-z)(IxBr1-x) solid solutions without need for binary chalcogenide precursors. This approach yields phase-pure films with precise control over morphology, composition, and crystallographic orientation. XRD analysis and DFT calculations confirm the formation of homogeneous solid solutions, while optoelectronic measurements reveal the distinct roles of halogen and chalcogen anions in tuning bandgap energy and carrier type, with Se shifting downwards the conduction band. The versatility of this synthesis technique enables morphology control ranging from compact films to rod-shaped microcrystals, expanding the functional adaptability of these materials. These findings offer a foundational framework for defect engineering and the scalable integration of chalcohalides in next-generation energy technologies, including photovoltaics, photocatalysis, thermoelectrics, and chemical sensing.

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arXiv2025

Stabilization of Metallic, Excitonic Insulator, and Superionic Phases in Helium-Rare Gas Compounds at Sub-Terapascal Pressures

Helium and rare gases (RG: Ne, Ar, Kr, Xe) are typically considered chemically inert, yet under the extreme pressures of planetary interiors they may form compounds with unexpected properties. Using crystal structure prediction and first-principles calculations, we mapped the phase diagram of binary He-RG systems up to $1$ TPa. We identify several previously unknown stoichiometric compounds that are both thermodynamically and vibrationally stable at sub-terapascal pressures, within the reach of modern high-pressure experiments. In particular, AHe$_{2}$ and AHe (A: Ar, Kr, Xe) adopt previously unreported orthorhombic, hexagonal and cubic phases that remain stable over wide pressure ranges. We further find that He-Xe systems host metallic and excitonic insulator phases at pressures nearly an order of magnitude lower than those required for pure helium, offering a pathway to realize these exotic quantum states experimentally. Finite-temperature simulations also reveal superionic He-Xe phases, in which helium ions diffuse either anisotropically or isotropically depending on the host lattice. These findings constitute the first prediction of helium-based systems that combine metallicity and superionicity, with profound implications for energy transport and planetary dynamo processes. Overall, our results demonstrate that mixing helium with heavier rare gases provides an effective strategy to stabilize metallic, excitonic insulator, and superionic phases at experimentally accessible pressures, opening new research directions for condensed matter physics and planetary science.

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arXiv2025

Fingerprinting Organic Molecules for the Inverse Design of Two-Dimensional Hybrid Perovskites with Target Energetics

Artificial intelligence (AI)-assisted workflows have transformed materials discovery, enabling rapid exploration of chemical spaces of functional materials. Endowed with extraordinary optoelectronic properties, two-dimensional (2D) hybrid perovskites represent an exciting frontier, but current efforts to design 2D perovskites rely heavily on trial-and-error and expert intuition approaches, leaving most of the chemical space unexplored and compromising the design of hybrid materials with desired properties. Here, we introduce an inverse design workflow for Dion-Jacobson perovskites that is built on an invertible fingerprint representation for millions of conjugated diammonium organic spacers. By incorporating high-throughput density functional theory (DFT) calculations, interpretable machine learning, and synthesis feasibility screening, we identified new organic spacer candidates with deterministic energy level alignment between the organic and the inorganic motifs in the 2D hybrid perovskites. These results highlight the power of integrating invertible, physically meaningful molecular representations into AI-assisted design, streamlining the property-targeted design of hybrid materials.

cond-mat.mtrl-sci↗

arXiv2025

Band-Gap Tunability in Anharmonic Perovskite-like Semiconductors Driven by Polar Electron-Phonon Coupling

The ability to finely tune optoelectronic properties in semiconductors is crucial for the development of advanced technologies, ranging from photodetectors to photovoltaics. In this work, we propose a novel strategy to achieve such tunability by utilizing electric fields to excite low-energy polar optical phonon modes, which strongly couple to electronic states in anharmonic semiconductors. We conducted a high-throughput screening of over $10,000$ materials, focusing on centrosymmetric compounds with imaginary polar phonon modes and suitable band gaps, and identified $310$ promising candidates with potential for enhanced optoelectronic tunability. From this set, three perovskite-like compounds --Ag$_3$SBr, BaTiO$_3$, and PbHfO$_3$-- were selected for in-depth investigation based on their contrasting band-gap behavior with temperature. Using first-principles calculations, \textit{ab initio} molecular dynamics simulations, tight-binding models, and anharmonic Fr\"ohlich theory, we analyzed the underlying physical mechanisms. Our results show that polar phonon distortions can induce substantial band-gap modulations at ambient conditions, including reductions of up to $70\%$ in Ag$_3$SBr and increases of nearly $23\%$ in BaTiO$_3$, relative to values calculated at zero temperature, while PbHfO$_3$ exhibits minimal change. These contrasting responses arise from distinct electron-phonon coupling mechanisms and orbital hybridization at the band edges. This work establishes key design principles for harnessing polar lattice dynamics to engineer tunable optoelectronic properties, paving the way for adaptive technologies such as wavelength-selective optical devices and solar absorbers.

cond-mat.mtrl-sci↗

arXiv2025

Machine Learning-Guided Discovery of Temperature-Induced Solid-Solid Phase Transitions in Inorganic Materials

Predicting solid-solid phase transitions remains a long-standing challenge in materials science. Solid-solid transformations underpin a wide range of functional properties critical to energy conversion, information storage, and thermal management technologies. However, their prediction is computationally intensive due to the need to account for finite-temperature effects. Here, we present an uncertainty-aware machine-learning-guided framework for high-throughput prediction of temperature-induced polymorphic phase transitions in inorganic crystals. By combining density functional theory calculations with graph-based neural networks trained to estimate vibrational free energies, we screened a curated dataset of approximately 50,000 inorganic compounds and identified over 2,000 potential solid-solid transitions within the technologically relevant temperature interval 300-600 K. Among our key findings, we uncover numerous phase transitions exhibiting large entropy changes (> 300 J K$^{-1}$ kg$^{-1}$), many of which occur near room temperature hence offering strong potential for solid-state cooling applications. We also identify $21$ compounds that exhibit substantial relative changes in lattice thermal conductivity (20-70%) across a phase transition, highlighting them as promising thermal switching materials. Validation against experimental observations and first-principles calculations supports the robustness and predictive power of our approach. Overall, this work establishes a scalable route to discover functional phase-change materials under realistic thermal conditions, and lays the foundation for future high-throughput studies leveraging generative models and expanding open-access materials databases.

cond-mat.mtrl-sci↗

arXiv2025

Parallel Exploration of the Optoelectronic Properties of (Sb,Bi)(S,Se)(Br,I) Chalcohalides

Chalcohalides are an emerging family of semiconductors with irresistible material properties, shaped by the intricate interplay between their unique structural chemistry and vibrational dynamics. Despite their promise for next-generation solar energy conversion devices, their intrinsic optoelectronic properties remain largely unexplored. Here, we focus on the (Sb,Bi)(S,Se)(Br,I) system, a subset of compounds that share the same quasi-1D crystal structure. Using a two-step physical vapor deposition (PVD) process, we synthesize the eight ternary chalcohalide compounds, demonstrating bandgaps ranging from 1.38 to 2.08 eV with sharp, single-component photoluminescence (PL) peaks. In a parallel exploration of carrier dynamics and intrinsic electron-phonon interactions -- comprehensively studied using power-, temperature-dependent, and time-resolved PL measurements -- we map their direct impact on optoelectronic performance. Supported by first-principles density functional theory (DFT) defect calculations, we establish clear structure-property relations, identifying solid-solutions engineering as an effective means to fine-tune the native phonon structures and further suppress non-radiative recombination. This study provides a blueprint for optimizing chalcohalides as high-efficiency materials across a wide range of optoelectronic applications.

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arXiv2025

Chalcogen Vacancies Rule Charge Recombination in Pnictogen Chalcohalide Solar-Cell Absorbers

Pnictogen chalcohalides (MChX, M = Bi, Sb; Ch = S, Se; X = I, Br) represent an emerging class of nontoxic photovoltaic absorbers, valued for their favorable synthesis conditions and excellent optoelectronic properties. Despite their proposed defect tolerance, stemming from the antibonding nature of their valence and conduction bands, their experimentally reported power conversion efficiencies remain below 10%, far from the ideal Shockley-Queisser limit of 30%. Using advanced first-principles calculations and defect sampling techniques, we uncover a complex point-defect landscape in MChX materials, exemplified by BiSeI. Previously overlooked selenium vacancies are identified as critical nonradiative charge-recombination centers, which exist in high concentrations and, although exhibit modest capture coefficients, can reduce the maximum power conversion efficiency of BiSeI down to 24%. We argue that such detrimental effects can be mitigated by cation-poor synthesis conditions and strategic anion substitutions. Building on these insights, and supported by further simulations, we predict BiSBr to be a more defect-tolerant light absorber. This study not only identifies efficiency-limiting factors in MChX but also provides a roadmap for their improvement, paving the way for next-generation solution-processed chalcogenide photovoltaics.

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arXiv2025

Molecular origins of colossal barocaloric effects in plastic crystals

In recent years, orientationally disordered crystals, or plastic crystals, have transformed the field of solid-state cooling due to the significant latent heat and entropy changes associated with their temperature induced molecular order-disorder phase transition, which can produce colossal caloric effects under external field stimuli. However, the molecular mechanisms underlying these huge caloric effects remain inadequately understood, and general principles for enhancing the performance of caloric plastic crystals are lacking. Previous studies have predominantly focused on molecular rotations, overlooking other potentially critical factors, such as lattice vibrations and molecular conformations. In this study, we employ classical molecular dynamics (MD) simulations to both replicate and elucidate the microscopic origins of the experimentally observed colossal barocaloric (BC) effects -- those driven by hydrostatic pressure -- in the archetypal plastic crystal neopentyl glycol (NPG). Our MD simulations demonstrate that in NPG, the combined BC response and phase-transition entropy changes arising from lattice vibrations and molecular conformations are nearly equal to those from molecular reorientations, contributing 45% and 55%, respectively. These findings suggest that, alongside hydrogen bonding -- which directly impacts molecular rotational dynamics -- lattice vibrational and molecular structural features, often overlooked, must be integrated into the rational design and modeling of advanced caloric plastic crystals. These insights are not only of significant fundamental interest but also essential for driving the development of next-generation solid-state refrigeration technologies.

cond-mat.mtrl-sci↗

arXiv2024

Giant Electron-Phonon Coupling Induced Band-Gap Renormalization in Anharmonic Silver Chalcohalide Antiperovskites

Silver chalcohalide antiperovskites (CAP), Ag$_{3}$XY (X = S, Se; Y = Br, I), are a family of highly anharmonic inorganic compounds with great potential for energy applications. However, a substantial and unresolved discrepancy exists between the optoelectronic properties predicted by theoretical first-principles methods and those measured experimentally at room temperature, hindering the fundamental understanding and rational engineering of CAP. In this work, we employ density functional theory, tight-binding calculations, and anharmonic Fr\"ohlich theory to investigate the optoelectronic properties of CAP at finite temperatures. Near room temperature, we observe a giant band-gap ($E_{g}$) reduction of approximately $20$-$60$\% relative to the value calculated at $T = 0$ K, bringing the estimated $E_{g}$ into excellent agreement with experimental measurements. This relative $T$-induced band-gap renormalization is roughly twice the largest value previously reported in the literature for similar temperature ranges. Low-energy optical polar phonon modes, which break inversion symmetry and promote the overlap between silver and chalcogen $s$ electronic orbitals in the conduction band, are identified as the primary contributors to this giant $E_{g}$ reduction. Furthermore, when considering temperature effects, the optical absorption coefficient of CAP increases by nearly an order of magnitude for visible light frequencies. These insights not only bridge a crucial gap between theory and experiment but also open pathways for future technologies where temperature, electric fields, or light dynamically tailor optoelectronic behavior, positioning CAP as a versatile platform for next-generation energy applications.

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