arXiv subjects

Cibrán López

Publications and source records attributed to Cibrán López.

13 recordsLinked to original sources

arXiv2026

Surface Physics of Low-Dimensional Pnictogen Chalcohalides and Its Impact on Photovoltaic Technologies

Low-dimensional pnictogen chalcohalides (MChX; M = Bi, Sb; Ch = S, Se; X = I, Br) are promising non-toxic semiconductors for solar energy conversion, yet the power conversion efficiencies of MChX solar cells remain below 10%, far from their Shockley-Queisser limit of $\sim$30%. Efficient charge extraction depends critically on the band-edge alignment between the absorber and the charge-selective contacts, which is a surface property. Here, we combine first-principles calculations with device-level modelling to investigate the surface energetics and band alignments across the MChX family. An exhaustive sampling of crystal orientations, including low-symmetry facets, identifies the (011) and (010) surfaces as the most stable terminations in all eight compounds. Their formation energies differ by only $\approx 0.01$-$0.03\mathrm{J\,m^{-2}}$, yet their band edges are shifted rigidly with respect to each other by up to $0.8$eV. Drift-diffusion simulations of BiSBr show that the coexistence of such facets can reduce the open-circuit voltage by up to $0.6$V, even in an otherwise ideal, defect-free absorber. Surface orientation thus emerges as a hidden design parameter for charge extraction. This study may help to explain the gap between the theoretical and experimental efficiencies of MChX solar cells, and it provides new design principles for MChX-based photovoltaic and photocatalytic technologies.

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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

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

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 Fröhlich 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.

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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.

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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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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 Fröhlich 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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arXiv2024

Crystal Structure Prediction and Phase Stability in Highly Anharmonic Silver-Based Chalcohalide Anti-Perovskites

Silver-based chalcohalide anti-perovskites (CAP), Ag$_{3}$BC (B = S, Se; C = Cl, Br, I), represent an emerging family of energy materials with intriguing optoelectronic, vibrational and ionic transport properties. However, the structural features and phase stability of CAP remain poorly investigated to date, hindering their fundamental understanding and potential integration into technological applications. Here we employ theoretical first-principles methods based on density functional theory to fill this knowledge gap. Through crystal structure prediction techniques, ab initio molecular dynamics simulations, and quasi-harmonic free energy calculations, we unveil a series of previously overlooked energetically competitive phases and temperature-induced phase transitions for all CAP. Specifically, we identify a new cubic $P2_{1}3$ structure as the stable phase of all CAP containing S both at zero temperature and $T \neq 0$ K conditions. Consequently, our calculations suggest that the cubic $Pm\overline{3}m$ phase identified in room-temperature X-ray diffraction experiments is likely to be metastable. Furthermore, for CAP containing Se, we propose different orthorhombic ($Pca2_{1}$ and $P2_{1}2_{1}2_{1}$) and cubic ($I2_{1}3$) structures as the ground-state phases and reveal several phase transformations induced by temperature. This theoretical investigation not only identifies new candidate ground-state phases and solid-solid phase transformations for all CAP but also provides insights into potential stability issues affecting these highly anharmonic superionic materials.

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arXiv2023

How concerted are ionic hops in inorganic solid-state electrolytes?

Despite being fundamental to the understanding of solid-state electrolytes (SSE), little is known on the degree of coordination between mobile ions in diffusive events. Thus far, identification of concerted ionic hops mostly has relied on the analysis of spatio-temporal pair correlation functions obtained from atomistic molecular dynamics (MD) simulations. However, this type of analysis neither allows for quantifying particle correlations beyond two body nor determining concerted ionic hop mechanisms, thus hindering a detailed comprehension and possible rational design of SSE. Here, we introduce an unsupervised k-means clustering approach able to identify ion-hopping events and correlations between many mobile ions, and apply it to a comprehensive ab initio MD database comprising several families of inorganic SSE and millions of ionic configurations. It is found that despite two-body interactions between mobile ions are largest, higher-order $n$-ion ($2 < n$) correlations are most frequent. Specifically, we prove an universal exponential decaying law for the probability density function governing the number of concerted mobile ions. For the particular case of Li-based SSE, it is shown that the average number of correlated mobile ions amounts to $10 \pm 5$ and that this result is practically independent of temperature. Interestingly, our data-driven analysis reveals that fast-ion diffusion strongly and positively correlates with ample hopping lengths and long hopping spans but not with high hopping frequencies and short interstitial residence times. Finally, it is shown that neglection of many-ion correlations generally leads to a modest overestimation of the hopping frequency that roughly is proportional to the average number of correlated mobile ions.

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arXiv2022

Universal ion-transport descriptors and classes of inorganic solid-state electrolytes

Solid-state electrolytes (SSE) with high ion conductivity are pivotal for the development and large-scale adoption of green-energy conversion and storage technologies such as fuel cells, electrocatalysts and solid-state batteries. Yet, SSE are extremely complex materials for which general rational design principles remain indeterminate. Here, we unite first-principles materials modelling, computational power and modern data analysis techniques to advance towards the solution of such a fundamental and technologically pressing problem. Our data-driven survey reveals that the correlations between ion diffusivity and other materials descriptors in general are monotonic, although not necessarily linear, and largest when the latter are of vibrational nature and explicitly incorporate anharmonic effects. Surprisingly, principal component and k-means clustering analysis show that elastic and vibrational descriptors, rather than the usual ones related to chemical composition and ion mobility, are best suited for reducing the high complexity of SSE and classifying them into universal classes. Our findings highlight the need of considering databases that incorporate temperature effects to improve our understanding of SSE and point towards a generalized approach to the design of energy materials.

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