arXiv subjects

Aron Walsh

Publications and source records attributed to Aron Walsh.

At least 19 recordsLinked to original sources

arXiv2026

Thermal history controls the optoelectronic response of lead halide perovskites through structure and dynamics

Lead halide perovskites are promising optoelectronic materials for photovoltaics, light emission and detection. Their efficiencies in PV now approach the detailed-balance limit, leaving stability as the principal barrier. The intrinsic instabilities studied to date centre on ionic motion within a fixed, homogeneous lattice. Here we identify a further source of intrinsic structural instability, hidden in the lattice dynamics. Mapping caesium, methylammonium and formamidinium-based compositions with Cl, Br, I and mixed X-sites through all accessible phases, using single crystal X-ray and neutron diffuse scattering, machine-learning-assisted molecular dynamics, a phenomenological octahedral tilt model and hyperspectral photoluminescence, we find that nearly every composition hosts equilibrium local structural fluctuations: dynamic nanodomains of correlated octahedral tilts, a few nanometres in size, that locally break the crystallographic symmetry. Three complementary levers control them. The A-site cation sets their symmetry, shape and anisotropy, from sparse, isotropic and tetragonal in formamidinium-based compositions to dense, anisotropic and orthorhombic in nominally cubic caesium-based ones, the most locally disordered we studied. The halide controls the dynamic disorder and the phase-transition sequence. Thermal history is the third: different ramp rates drive nominally identical compositions into distinct crystallographic phases, each with its own hidden local order. In MAPbI3, the heating rate alone changes the photoluminescence quantum efficiency across the phase transition. Because these transitions lie within device operating ranges, from terrestrial thermal cycling to the extremes of space, thermal history may shape the local structure, and hence the optoelectronic response, throughout fabrication and operation, establishing it as a design variable alongside composition.

cond-mat.mtrl-sci↗

arXiv2026

Chemical filters for ultra-high-throughput materials screening and generation

Generative artificial intelligence is rapidly transforming materials design by enabling de novo exploration of immense chemical spaces. Yet a large proportion of AI-generated compositions remain implausible, violating established chemical principles, which limits the reliability and interpretability of generative materials design. Here, we introduce a chemical validity operator that recasts heuristic chemical rules as a configurable algorithmic prior for evaluating and guiding generative materials discovery. Built on the open-source SMACT package, a data-informed oxidation-state model exposes tunable thresholds, allowing users to interpolate continuously between permissive and conservative chemical constraints, while supporting both exploratory and conservative materials-design workflows. Benchmarking six state-of-the-art generative models for inorganic crystals shows that most reproduce stoichiometry but under-represent realistic oxidation-state combinations, and that filtering removes compositions reliant on rarely observed oxidation states while preserving low-energy compounds near the convex hull. Beyond screening, the same operator can also serve as a reinforcement-learning reward, steering a latent diffusion model towards chemically grounded compositions. By encoding chemical heuristics and observations, this work establishes a foundation for oxidation-state-aware generative models.

cond-mat.mtrl-sci↗

arXiv2026

Substitution-Based Analysis of Structural Novelty for Generative Models of Materials

There has been rapid progress in generative artificial intelligence (AI) models for inorganic crystal design, which can efficiently generate large numbers of candidate compounds after being trained on databases of known crystals. However, it remains unclear whether they genuinely expand the accessible materials search space beyond conventional strategies such as elemental substitution within known structure types. We address this question by developing a workflow to assess whether AI-generated crystals are duplicates of training structures, reproducible by elemental substitution, or unmatched by either criterion. Applying this workflow to representative generative models reveals that 81-92% of chemically valid and metastable generated crystals are either training duplicates or substitution-derived structures. This tendency is particularly strong in high-symmetry crystal systems, even though many possible structural prototypes remain unexplored. Further analysis of the underlying structural fingerprints shows that low-symmetry structures beyond duplication or substitution can be interpreted as interpolation in training-data-rich regions, while high-symmetry duplicates appear to result from memorisation in training-sparse regions. Our findings highlight a limitation in the current generation of models that exhibit a bias towards known structural prototypes in the high symmetry regions, but enable wider exploration of the low-symmetry structural space.

cs.LG↗

arXiv2026

Kinetically Arrested Twin-Domain State in Formamidinium Lead Iodide

Hybrid lead halide perovskites exhibit a delicate interplay between average crystallographic symmetry, local structural disorder and A-site orientational dynamics, giving rise to unusual vibrational and electronic behaviour. Here, we combine large-scale molecular dynamics with a density-functional-theory-accurate machine learning force field to resolve the structural dynamics of perovskites across mesoscopic length scales. In formamidinium lead iodide FAPbI$_{3}$, we identify a high-temperature $\alpha$ phase with dynamic local order and correlated tilt nanodomains, an ordered $\gamma$ phase with long-range $a^{+}a^{+}a^{+}$ tilt coherence, and, below $\sim$100 K, a history-dependent $\gamma'$ state consisting of locally $\gamma$-like nanoscale regions separated by sharp twin-like boundaries. This low-temperature disordered state is not a distinct bulk polymorph, but a kinetically arrested metastable twin-domain network selected by the interplay between shallow tilt energetics and slowing FA reorientation. This picture provides a consistent explanation for the low-temperature diffuse scattering features observed experimentally, and accounts for the broadened low-energy vibrational response found in the simulations. Furthermore, this unique structural landscape imprints a spatially varying electronic disorder that directly impacts macroscopic optoelectronic properties, evidenced by an anomalous increase in the Urbach energy at low temperatures. Our results reconcile the debated low-temperature behaviour of FAPbI$_{3}$ in terms of competition between ordered and arrested structural states, and show more broadly that in hybrid perovskites the organic cation can actively select the macroscopic structural and electronic response through its reorientation kinetics, placing thermal history on equal footing with composition as a determinant of structural and optoelectronic properties.

cond-mat.mtrl-sci↗

arXiv2026

Multi-fidelity Machine Learning Interatomic Potentials for Charged Point Defects

Machine learning interatomic potentials (MLIPs) can now reproduce the energy, forces and stresses of bulk materials with high accuracy compared to first-principles calculations. The description of imperfections, where coordination environments and electron counts deviate from those found in pristine reference structures, remains a challenge. We find that the current generation of foundation MLIPs do not describe the defect physics of the semiconductor Sb2Se3. We introduce global defect charge embeddings that distinguish the bonding characteristics of different charge states. We further employ a multi-fidelity approach that combines low-cost (semi-local exchange-correlation functional) reference data with high-quality (non-local hybrid functional) energies and forces that describe well the subtleties of the defect energy landscape. The resulting defect-capable force fields can find stable structural configurations and predict defect thermodynamics in quantitative agreement with direct quantum mechanical calculations, at a fraction of the computational cost.

cond-mat.mtrl-sci↗

arXiv2026

MADE: Benchmark Environments for Closed-Loop Materials Discovery

Existing benchmarks for computational materials discovery primarily evaluate static predictive tasks or isolated computational sub-tasks. While valuable, these evaluations neglect the inherently iterative and adaptive nature of scientific discovery. We introduce MAterials Discovery Environments (MADE), a novel framework for benchmarking end-to-end autonomous materials discovery pipelines. MADE simulates closed-loop discovery campaigns in which an agent or algorithm proposes, evaluates, and refines candidate materials under a constrained oracle budget, capturing the sequential and resource-limited nature of real discovery workflows. We formalize discovery as a search for thermodynamically stable compounds relative to a given convex hull, and evaluate efficacy and efficiency via comparison to baseline algorithms. The framework is flexible; users can compose discovery agents from interchangeable components such as generative models, filters, and planners, enabling the study of arbitrary workflows ranging from fixed pipelines to fully agentic systems with tool use and adaptive decision making. We demonstrate this by conducting systematic experiments across a family of systems, enabling ablation of components in discovery pipelines, and comparison of how methods scale with system complexity.

cs.LG↗

arXiv2025

Breaking the 800 mV open-circuit voltage barrier in antimony sulfide photovoltaics

Sb2S3 is a promising material for low-toxicity, high-stability next-generation photovoltaics. Despite high optical limits in efficiency, progress in improving its device performance has been limited by severe voltage losses. Recent spectroscopic investigations suggest that self-trapping occurs in Sb2S3, limiting the open-circuit voltage (Voc) to a maximum of approximately 800 mV, which is the level the field has asymptotically approached. In this work, we surpass this voltage barrier through reductions in the defect density in Sb2S3 thin films by modulating the growth mechanism in chemical bath deposition using citrate ligand additives. Deep level transient spectroscopy identifies two deep traps 0.4-0.7 eV above the valence band maximum, and, through first-principles calculations, we identify these to likely be S vacancies, or Sb on S anti-sites. The concentrations of these traps are lowered by decreasing the grain boundary density from 1114+/-52 nm/um2 to 585+/-10 nm/um2, and we achieve a Voc of 824 mV, the record for Sb2S3 solar cells. This work addresses the debate in the field around whether Sb2S3 is limited by defects or self-trapping, showing that it is possible to improve the performance towards the radiative limit through careful defect engineering.

cond-mat.mtrl-sci↗

arXiv2025

Platonic representation of foundation machine learning interatomic potentials

Foundation machine learning interatomic potentials (MLIPs) are trained on overlapping chemical spaces, yet their latent representations remain model-specific. Here, we show that independently developed MLIPs exhibit statistically consistent geometric organisation of atomic environments, which we term the Platonic representation. By projecting embeddings relative to a set of atomic anchors, we unify the latent spaces of seven MLIPs (spanning equivariant, non-equivariant, conservative, and non-conservative architectures) into a common metric space that preserves chemical periodicity and structural invariants. This unified framework enables direct cross-model optimal transport, interpretable embedding arithmetic, and the detection of representational biases. Furthermore, we demonstrate that geometric distortions in this space can indicate physical prediction failures, including symmetry breaking and incorrect phonon dispersions. Our results show that the latent spaces of diverse MLIPs present consistent statistical geometry shaped by shared physical and chemical constraints, suggesting that the Platonic representation offers a practical route toward interoperable, comparable, and interpretable foundation models for materials science.

cond-mat.mtrl-sci↗

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.

cond-mat.mtrl-sci↗

arXiv2025

Crystalyse: a multi-tool agent for materials design

We present Crystalyse, an open, provenance-enforced scientific agent for computational materials design of inorganic crystals that orchestrates tools for compositional screening, crystal structure generation, and machine-learning force-field evaluation. Crystalyse offers three operating modes to trade exploration speed against validation depth: creative (rapid query), adaptive (context-aware routing) and rigorous (comprehensive checks). We release the underlying source code and evaluation scripts to enable plug-and-play use and development. In demonstrations on quaternary oxide exploration, sodium-ion cathode design, and lead-free indoor photovoltaic candidate generation, the agent integrates chemical compound generation with fast stability and property filters. Under adversarial testing, provenance enforcement eliminated material-property hallucinations (a broad adversarial suite pass rate reached 86% from a 57% baseline). Crystalyse provides an agentic artificial intelligence system that can complement existing materials design pipelines, assisting in hypothesis generation while preserving transparency and reproducibility.

cond-mat.mtrl-sci↗

arXiv2025

Guiding Generative Models to Uncover Diverse and Novel Crystals via Reinforcement Learning

Discovering functional crystalline materials entails navigating an immense combinatorial design space. While recent advances in generative artificial intelligence have enabled the sampling of chemically plausible compositions and structures, a fundamental challenge remains: the objective misalignment between likelihood-based sampling in generative modelling and targeted focus on underexplored regions where novel compounds reside. Here, we introduce a reinforcement learning framework that guides latent denoising diffusion models toward diverse and novel, yet thermodynamically viable crystalline compounds. Our approach integrates group relative policy optimisation with verifiable, multi-objective rewards that jointly balance creativity, stability, and diversity. Beyond de novo generation, we demonstrate enhanced property-guided design that preserves chemical validity, while targeting desired functional properties. This approach establishes a modular foundation for controllable AI-driven inverse design that addresses the novelty-validity trade-off across scientific discovery applications of generative models.

cs.LG↗

arXiv2025

Continuous SUN (Stable, Unique, and Novel) Metric for Generative Modeling of Inorganic Crystals

To address pressing scientific challenges such as climate change, increasingly sophisticated generative models are being developed to efficiently sample the large chemical space of potential functional materials. The proliferation of these models has necessitated the establishment of rigorous evaluation metrics. While uniqueness (U), novelty (N), and stability (S) of samples serve as standard metrics, their current formulations show several limitations. U and N rely on binary comparisons of crystals, rendering them dependent on heuristic thresholds, incapable of quantifying the degree of similarity, sensitive to atomic coordinate perturbations, and not invariant to sample permutation. Similarly, the binary assessment of S risks a premature exclusion of marginally unstable yet potentially novel candidates. These limitations are addressed by making the aforementioned metrics continuous. Furthermore, we integrate them into a unified metric ``continuous SUN" (cSUN), which offers a smoother score distribution and greater tunability than the conventional binary SUN metric. Experimental results demonstrate that our continuous metrics provide granular insights into sample distributions and facilitate the identification of the most promising candidates. Finally, the use of cSUN as a reward signal in reinforcement learning is explored, showing that its adjustable weighting scheme effectively mitigates reward hacking and avoids local minima.

cs.LG↗

arXiv2025

Polarization Domain Mapping From 4D-STEM Using Deep Learning

Polarization in ferroelectric domains arises from atomic-scale structural variations that govern macroscopic functionalities. The interfaces between these domains known as domain walls host distinct physical responses, making their identification and control critical. Four dimensional scanning transmission electron microscopy (4DSTEM) enables simultaneous acquisition of real and reciprocal-space information at the atomic scale, offering a powerful platform for domain mapping. However, conventional analyses rely on computationally intensive processing and manual interpretation, which are time consuming and prone to misalignment and diffraction artefacts. Here, we present a convolutional neural network that, with minimal training, classifies polarization directions from diffraction data and segments domains in real space. We further introduce an adaptive sampling strategy that prioritizes images from domain wall regions, reducing the number of training images required while improving accuracy and interpretability. We demonstrate this approach for domain mapping in ferroelectric boracite, Cu3B7O13Cl.

cond-mat.mtrl-sci↗

arXiv2025

MLIP Arena: Advancing Fairness and Transparency in Machine Learning Interatomic Potentials via an Open, Accessible Benchmark Platform

Machine learning interatomic potentials (MLIPs) have revolutionized molecular and materials modeling, but existing benchmarks suffer from data leakage, limited transferability, and an over-reliance on error-based metrics tied to specific density functional theory (DFT) references. We introduce MLIP Arena, a benchmark platform that evaluates force field performance based on physics awareness, chemical reactivity, stability under extreme conditions, and predictive capabilities for thermodynamic properties and physical phenomena. By moving beyond static DFT references and revealing the important failure modes of current foundation MLIPs in real-world settings, MLIP Arena provides a reproducible framework to guide the next-generation MLIP development toward improved predictive accuracy and runtime efficiency while maintaining physical consistency. The Python package and online leaderboard are available at https://github.com/atomind-ai/mlip-arena.

physics.chem-ph↗

arXiv2025

Dynamic Vacancy Levels in CsPbCl3 Obey Equilibrium Defect Thermodynamics

Halide vacancies are the dominant point defects in perovskites with VCl identified as a detrimental trap for the optoelectronic performance of CsPbCl3, with applications ranging from photodetectors to solar cells. Understanding these defects under operating conditions is key since their electronic levels exhibit large thermal fluctuations that challenge the validity of static 0 K models. However, quantitative modelling of defect processes requires hybrid density functional theory with spin-orbit coupling, which is too expensive for direct molecular dynamic simulations. To address this, we train a multi-task machine learning force field to study VCl in orthorhombic CsPbCl3 at 300 K. While we observe strong oscillations in the optical transition level arising from the soft potential energy surface, neither the non-radiative capture barriers nor the thermodynamic charge transition levels are affected. Our results reveal that VCl is not responsible for the non-radiative losses previously assumed. Instead, its impact on performance arises from other mechanisms, such as limiting the open-circuit voltage and promoting ionic migration. Our findings demonstrate that, despite strong dynamical effects in halide perovskites, the conventional static formalism of defect theory remains valid for predicting thermodynamic behavior, providing a sound basis for the design of high-performance energy materials.

cond-mat.mtrl-sci↗

arXiv2025

Spectroscopic Signatures of Structural Disorder and Electron-Phonon Interactions in Trigonal Selenium Thin Films for Solar Energy Harvesting

Selenium is experiencing renewed interest as a elemental semiconductor for a range of optoelectronic and energy applications due to its irresistibly simple composition and favorable wide bandgap. However, its high volatility and low radiative efficiency make it challenging to assess structural and optoelectronic quality, calling for advanced, non-destructive characterization methods. In this work, we employ a closed-space encapsulation strategy to prevent degradation during measurement and enable sensitive probing of vibrational and optoelectronic properties. Using temperature-dependent Raman and photoluminescence spectroscopy, we investigate grown-in stress, vibrational dynamics, and electron-phonon interactions in selenium thin films synthesized under nominally identical conditions across different laboratories. Our results reveal that short-range structural disorder is not intrinsic to the material, but highly sensitive to subtle processing variations, which strongly influence electron-phonon coupling and non-radiative recombination. We find that such structural disorder and grown-in stress likely promote the formation of extended defects, which act as dominant non-radiative recombination centers limiting carrier lifetime and open-circuit voltage in photovoltaic devices. These findings demonstrate that the optoelectronic quality of selenium thin films can be significantly improved through precise control of synthesis and post-deposition treatments, outlining a clear pathway toward optimizing selenium-based thin film technologies through targeted control of crystallization dynamics and microstructural disorder.

cond-mat.mtrl-sci↗

arXiv2025

Phase Stability and Transformations in Lead Mixed Halide Perovskites from Machine Learning Force Fields

Lead halide perovskites (APbX$_3$) offer tunable optoelectronic properties but feature an intricate phase-stability landscape. Here we employ on-the-fly data collection and an equivariant message-passing neural-network potential to perform large-scale molecular dynamics of three prototypical perovskite systems: CsPbX$_3$, MAPbX$_3$, and FAPbX$_3$. Integrating these simulations with the PDynA analysis toolkit, we resolve both equilibrium phase diagrams and dynamic structural evolution under varying temperature and halide-mixing conditions. Our findings reveal that the A-site cation strongly modulates octahedral tilt modes and phase pathways: MA$^+$ effectively "forbids" the beta-to-gamma transition in MAPbX$_3$ by requiring extensive molecular rearrangements and crystal rotation, whereas the debated low-temperature phase in FAPbX$_3$ is best represented as an Im$\bar{3}$ cubic phase with $a^+a^+a^+$ tilts. Additionally, small changes in halide composition and arrangement $\unicode{x2013}$ from uniform mixing to partial segregation $\unicode{x2013}$ alter tilt correlations. Segregated domains can even foster anomalous tilting modes that impede uniform phase transformations. These results highlight the multi-scale interplay between cation environment and halide distribution, offering a rational basis for tuning perovskite architectures toward improved phase stability.

cond-mat.mtrl-sci↗

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.

cond-mat.mtrl-sci↗