arXiv subjects

Nathan J. Szymanski

Publications and source records attributed to Nathan J. Szymanski.

12 recordsLinked to original sources

arXiv2026

Scalable machine learning framework for multiphase identification from powder X-ray diffraction

X-ray diffraction (XRD) is the primary tool for identifying crystalline phases following synthesis, but automated phase identification remains challenging, particularly for multiphase samples with overlapping peaks and experimental artifacts. While deep-learning methods have been proposed to improve upon classical search-match algorithms, most formulate phase identification as a single closed-set classification problem, requiring one shared model to discriminate among all candidate phases. Here we introduce GALAXI, which instead decouples the identification task into independent one-versus-all binary classifiers that each specialize in recognizing a single phase. These pre-trained classifiers first narrow the search space to a small set of plausible phases, which are then evaluated through Rietveld refinement to identify the combination of phases that best explains the full diffraction pattern. On a curated set of experimental patterns, GALAXI identifies the correct phases with a micro-F1 score of 0.935, outperforming classical search-match and prior deep-learning models. The method remains robust to common experimental artifacts, including low impurity phase fractions, small crystallite size, peak shifts, sample displacement, and texture, and performs well when applied to time-resolved in-situ XRD data from solid-state reactions. Moreover, because the phase-specific models are independent, GALAXI can expand to large reference libraries without retraining existing models. This modular architecture enables us to train classifiers for 64,594 structures from the Crystallography Open Database and deploy them through a public web interface at https://galaxi-xrd.com.

cond-mat.mtrl-sci↗

arXiv2026

Thermodynamic assessment of machine learning models for solid-state synthesis prediction

Machine learning models have recently emerged to predict whether hypothetical solid-state materials can be synthesized. These models aim to circumvent direct first-principles modeling of solid-state phase transformations, instead learning from large databases of successfully synthesized materials. Here, we assess the alignment of several recently introduced synthesis prediction models with material and reaction thermodynamics, quantified by the energy with respect to the convex hull and a metric accounting for thermodynamic selectivity of enumerated synthesis reactions. A dataset of successful synthesis recipes was used to determine the likely bounds on both quantities beyond which materials can be deemed unlikely to be synthesized. With these bounds as context, thermodynamic quantities were computed using the CHGNet foundation potential for thousands of new hypothetical materials generated using the Chemeleon generative model. Four recently published machine learning models for synthesizability prediction were applied to this same dataset, and the resultant predictions were considered against computed thermodynamics. We find these models generally overpredict the likelihood of synthesis, but some model scores do trend with thermodynamic heuristics, assigning lower scores to materials that are less stable or do not have an available synthesis recipe that is calculated to be thermodynamically selective. In total, this work identifies existing gaps in machine learning models for materials synthesis and introduces a new approach to assess their quality in the absence of extensive negative examples (failed syntheses).

cond-mat.mtrl-sci↗

arXiv2026

Cation vacancies mediate thermochemical water splitting with iron aluminates

Solar thermochemical water splitting enables hydrogen production by cycling metal oxides between reduced and oxidized states, typically through an oxygen vacancy mechanism. However, recent experimental work suggests that cation vacancies have a greater influence on the redox behavior of iron aluminate spinels used in water splitting. This remains debated, as calculations predict that such cation vacancies are thermodynamically unfavorable. In the current work, we show that Fe vacancies in (Fe$ζ$Al1-$ζ$)3O4 become accessible only when facilitated by inversion between Fe and Al. This antisite disorder lowers the formation energy of octahedral Fe vacancies in Al-rich spinels ($ζ$ = 1/3) from over 3 eV to just 0.62 eV when one third of the cation sites are inverted, allowing high Fe vacancy concentrations under oxidizing conditions. This mechanism supports high H2 yields up to 361 $μ$mol/g, consistent with experimental observations. Our findings support the notion that solar thermochemical water splitting can occur through a cation vacancy mechanism. They also clarify how site inversion, vacancy energetics, and defect interactions each contribute to redox performance, offering general design principles for identifying and optimizing materials that operate through cation vacancy cycling.

cond-mat.mtrl-sci↗

arXiv2025

Thermodynamics of proton insertion across the perovskite-brownmillerite transition in La0.5Sr0.5CoO3-δ

La$_{1-x}$Sr$_{x}$CoO3-$δ$ is a promising off-stoichiometric metal oxide that undergoes a topotactic perovskite ($δ$ = 0) to brownmillerite ($δ$ = 0.5) transition under electrochemical and thermochemical stimuli, with concomitant variations in its electrical, magnetic, thermal, and optical properties. Recent studies on thin-film cycling in electrochemical devices show incomplete reversibility of this transition, with significant acid-etching serving as a degradation mechanism. While earlier investigations examined the protonation of brownmillerite SrCoO2.5, the thermodynamics of protonation across the perovskite-to-brownmillerite transition remain poorly understood. In this work, we combine density functional theory calculations with predictions from universal machine-learning interatomic potentials to elucidate the energetics and implications of protonation across the transition for La0.5Sr0.5CoO3-$δ$. These calculations reveal negative hydrogen insertion energies and strong competition with oxygen vacancy formation across the transition for a wide range of conditions. The extent of protonation is primarily limited by the availability of Co 3d states to accommodate reduction by inserted hydrogen. Although hydrogen insertion is often thermodynamically favorable within a defect picture, a convex hull analysis of the resulting HyLa0.5Sr0.5CoO3-$δ$ phases reveals them to be unstable against decomposition into hydroxides among other products. This instability increases with hydrogen content and provides a thermodynamic basis for the acid-etching observed during electrochemical cycling. This work advances the fundamental understanding of protonation in La0.5Sr0.5CoO3-$δ$ and contextualizes experimental observations of related materials in the presence of moisture or H2.

cond-mat.mtrl-sci↗

arXiv2025

Computational search for materials having a giant anomalous Hall effect in the pyrochlore and spinel crystal structures

Ferromagnetic pyrochlore and spinel materials with topological flat bands are of interest for their potential to exhibit a giant anomalous Hall effect (AHE). In this work, we present computational predictions of stability and electronic structure for 448 compositions within the pyrochlore (A2B2O7) and spinel (AB2O4) frameworks. Of these, 92 are predicted to be thermodynamically stable or close (< 100 meV/atom) to the convex hull, with trends deviating from expectations based on ionic radius-ratio rules. Thirteen are predicted to adopt a ferromagnetic ground state among the collinear configurations considered. Two additional materials meeting these criteria were also identified from open materials databases. Calculations of anomalous Hall angles (AHA) and conductivities reveal that 11 of the screened materials are promising candidates for spintronic applications requiring high electronic conductivity and a giant AHE. Our results suggest that the AHA can be further enhanced by tuning the Fermi level, for example through chemical doping. Using this approach, we identify five materials whose AHA exceed 0.2 under the approximation of collinear magnetism. Notably, Ag2Pt2O7 exhibits a high AHA of 0.405 when its Fermi level is optimized. These findings provide a roadmap for the targeted synthesis of new pyrochlore and spinel compounds with enhanced AHE properties. They also broaden the compositional design space for these structures and support the discovery of high-performance materials for next-generation spintronic applications.

cond-mat.mtrl-sci↗

arXiv2025

Establishing baselines for generative discovery of inorganic crystals

Generative artificial intelligence offers a promising avenue for materials discovery, yet its advantages over traditional methods remain unclear. In this work, we introduce and benchmark two baseline approaches - random enumeration of charge-balanced prototypes and data-driven ion exchange of known compounds - against four generative techniques based on diffusion models, variational autoencoders, and large language models. Our results show that established methods such as ion exchange are better at generating novel materials that are stable, although many of these closely resemble known compounds. In contrast, generative models excel at proposing novel structural frameworks and, when sufficient training data is available, can more effectively target properties such as electronic band gap and bulk modulus. To enhance the performance of both the baseline and generative approaches, we implement a post-generation screening step in which all proposed structures are passed through stability and property filters from pre-trained machine learning models including universal interatomic potentials. This low-cost filtering step leads to substantial improvement in the success rates of all methods, remains computationally efficient, and ultimately provides a practical pathway toward more effective generative strategies for materials discovery. By establishing baselines for comparison, this work highlights opportunities for continued advancement of generative models, especially for the targeted generation of novel materials that are thermodynamically stable.

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arXiv2025

Topological descriptors for the electron density of inorganic solids

Descriptors play an important role in data-driven materials design. While most descriptors of crystalline materials emphasize structure and composition, they often neglect the electron density - a complex yet fundamental quantity that governs material properties. Here, we introduce Betti curves as topological descriptors that compress electron densities into compact representations. Derived from persistent homology, Betti curves capture bonding characteristics by encoding components, cycles, and voids across varied electron density thresholds. Machine learning models trained on Betti curves outperform those trained on raw electron densities by an average of 33 percentage points in classifying structure prototypes, predicting thermodynamic stability, and distinguishing metals from non-metals. Shannon entropy calculations reveal that Betti curves retain comparable information content to electron density while requiring two orders of magnitude less data. By combining expressive power with compact representation, Betti curves highlight the potential of topological data analysis to advance materials design.

cond-mat.mtrl-sci↗

arXiv2024

AlabOS: A Python-based Reconfigurable Workflow Management Framework for Autonomous Laboratories

The recent advent of autonomous laboratories, coupled with algorithms for high-throughput screening and active learning, promises to accelerate materials discovery and innovation. As these autonomous systems grow in complexity, the demand for robust and efficient workflow management software becomes increasingly critical. In this paper, we introduce AlabOS, a general-purpose software framework for orchestrating experiments and managing resources, with an emphasis on automated laboratories for materials synthesis and characterization. AlabOS features a reconfigurable experiment workflow model and a resource reservation mechanism, enabling the simultaneous execution of varied workflows composed of modular tasks while eliminating conflicts between tasks. To showcase its capability, we demonstrate the implementation of AlabOS in a prototype autonomous materials laboratory, A-Lab, with around 3,500 samples synthesized over 1.5 years.

cond-mat.mtrl-sci↗

arXiv2023

Selective formation of metastable polymorphs in solid-state synthesis

Metastable polymorphs often result from the interplay between thermodynamics and kinetics. Despite advances in predictive synthesis for solution-based techniques, there remains a lack of methods to design solid-state reactions targeting metastable materials. Here, we introduce a theoretical framework to predict and control polymorph selectivity in solid-state reactions. This framework presents reaction energy as a rarely used handle for polymorph selection, which influences the role of surface energy in promoting the nucleation of metastable phases. Through in situ characterization and density functional theory calculations on two distinct synthesis pathways targeting LiTiOPO4, we demonstrate how precursor selection and its effect on reaction energy can effectively be used to control which polymorph is obtained from solid-state synthesis. A general approach is outlined to quantify the conditions under which metastable polymorphs are experimentally accessible. With comparison to historical data, this approach suggests that using appropriate precursors could enable the synthesis of many novel materials through selective polymorph nucleation.

cond-mat.mtrl-sci↗

arXiv2023

Autonomous decision making for solid-state synthesis of inorganic materials

To aid in the automation of inorganic materials synthesis, we introduce an algorithm (ARROWS3) that guides the selection of precursors used in solid-state reactions. Given a target phase, ARROWS3 iteratively proposes experiments and learns from their outcomes to identify an optimal set of precursors that leads to maximal yield of that target. Initial experiments are selected based on thermochemical data collected from first principles calculations, which enable the identification of precursors exhibiting large thermodynamic force to form the desired target. Should the initial experiments fail, their associated reaction paths are determined by sampling a range of synthesis temperatures and identifying their products. ARROWS3 then uses this information to pinpoint which intermediate reactions consume most of the available free energy associated with the starting materials. In subsequent experimental iterations, precursors are selected to avoid such unfavorable reactions and therefore maintain a strong driving force to form the target. We validate this approach on three experimental datasets containing results from more than 200 distinct synthesis procedures. When compared to several black-box optimization algorithms, ARROWS3 identifies the most effective set of precursors for each target while requiring substantially fewer experimental iterations. These findings highlight the importance of using domain knowledge in the design of optimization algorithms for materials synthesis, which are critical for the development of fully autonomous research platforms.

cond-mat.mtrl-sci↗

arXiv2022

A universal synthetic dataset for machine learning on spectroscopic data

To assist in the development of machine learning methods for automated classification of spectroscopic data, we have generated a universal synthetic dataset that can be used for model validation. This dataset contains artificial spectra designed to represent experimental measurements from techniques including X-ray diffraction, nuclear magnetic resonance, and Raman spectroscopy. The dataset generation process features customizable parameters, such as scan length and peak count, which can be adjusted to fit the problem at hand. As an initial benchmark, we simulated a dataset containing 35,000 spectra based on 500 unique classes. To automate the classification of this data, eight different machine learning architectures were evaluated. From the results, we shed light on which factors are most critical to achieve optimal performance for the classification task. The scripts used to generate synthetic spectra, as well as our benchmark dataset and evaluation routines, are made publicly available to aid in the development of improved machine learning models for spectroscopic analysis.

cs.LG↗

arXiv2021

A probabilistic deep learning approach to automate the interpretation of multi-phase diffraction spectra

Autonomous synthesis and characterization of inorganic materials requires the automatic and accurate analysis of X-ray diffraction spectra. For this task, we designed a probabilistic deep learning algorithm to identify complex multi-phase mixtures. At the core of this algorithm lies an ensemble convolutional neural network trained on simulated diffraction spectra, which are systematically augmented with physics-informed perturbations to account for artifacts that can arise during experimental sample preparation and synthesis. Larger perturbations associated with off-stoichiometry are also captured by supplementing the training set with hypothetical solid solutions. Spectra containing mixtures of materials are analyzed with a newly developed branching algorithm that utilizes the probabilistic nature of the neural network to explore suspected mixtures and identify the set of phases that maximize confidence in the prediction. Our model is benchmarked on simulated and experimentally measured diffraction spectra, showing exceptional performance with accuracies exceeding those given by previously reported methods based on profile matching and deep learning. We envision that the algorithm presented here may be integrated in experimental workflows to facilitate the high-throughput and autonomous discovery of inorganic materials.

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