arXiv subjects

Samuel P. Gleason

Publications and source records attributed to Samuel P. Gleason.

6 recordsLinked to original sources

arXiv2025

LeMat-GenBench: A Unified Evaluation Framework for Crystal Generative Models

Generative machine learning (ML) models hold great promise for accelerating materials discovery through the inverse design of inorganic crystals, enabling an unprecedented exploration of chemical space. Yet, the lack of standardized evaluation frameworks makes it challenging to evaluate, compare, and further develop these ML models meaningfully. In this work, we introduce LeMat-GenBench, a unified benchmark for generative models of crystalline materials, supported by a set of evaluation metrics designed to better inform model development and downstream applications. We release both an open-source evaluation suite and a public leaderboard on Hugging Face, and benchmark 12 recent generative models. Results reveal that an increase in stability leads to a decrease in novelty and diversity on average, with no model excelling across all dimensions. Altogether, LeMat-GenBench establishes a reproducible and extensible foundation for fair model comparison and aims to guide the development of more reliable, discovery-oriented generative models for crystalline materials.

cs.LG↗

arXiv2025

LeMat-Synth: a multi-modal toolbox to curate broad synthesis procedure databases from scientific literature

Wide access to advanced experimental methods in materials science has given rise to an abundance of procedural knowledge, which is scattered across decades of scientific literature and recorded in unstructured formats that are challenging to analyze systematically. In this work, we present LeMat-Synth Parser, a modular, open-source, and multi-modal extraction toolbox that utilizes large language models (LLMs) and vision language models (VLMs) to automatically structure synthesis protocols and performance metrics extracted from both text and figures of publications. Applying LeMat-Synth Parser to 81K open-access publications, we curate LeMat-Synth, an extensive dataset of 58K synthesis procedures and to our knowledge the largest and most diverse structured inorganic materials synthesis dataset to date, covering 35 synthesis methods and 16 material classes based on a domain-specific ontology. We validate extraction quality against annotations by domain experts and a scalable LLM-as-a-judge framework, and benchmark a suite of models to identify optimal configurations and characterize cross-model biases. To demonstrate the extensibility of LeMat-Synth Parser, we apply it to two distinct domains. First, we link synthesis protocols and catalyst identity to thermocatalytic performance across a corpus of ammonia-decomposition publications. Second, we cross-validate text- and figure-reported critical transition temperatures across 1,384 superconductivity papers, then use the validated pipeline to recover the critical transition temperature for every composition in a sample series. We release LeMat-Synth Parser and the LeMat-Synth dataset openly on GitHub and Hugging Face

cs.DL↗

arXiv2024

CuXASNet: Rapid and Accurate Prediction of Copper L-edge X-Ray Absorption Spectra Using Machine Learning

In this work, we have developed CuXASNet, a dense neural network that predicts simulated Cu L-edge X-ray absorption spectra (XAS) from atomic structures. Featurization of the Cu local environment is performed using a component of M3GNet, a graph neural network developed for predicting the potential energy surface. CuXASNet is trained on simulated spectra from FEFF9 at the multiple scattering level of theory, and can predict the L3 and L2 edges for Cu sites to quantitative accuracy. To validate our approach, we compare 14 experimental spectra extracted from the literature with the predictions of CuXASNet. The agreement of CuXASNet with experiments is shown by an average MAE of 0.125 and an average Spearman's correlation coefficient of 0.891, which is comparable to FEFF9's values of 0.131 and 0.898 for the same metrics. As such, CuXASNet can rapidly generate a large number of L-edge XAS spectra at the same accuracy as FEFF9 simulations. This can be used as a drop-in replacement for multiple scattering codes for fast screening of candidate atomic structure models of a measured system. This model establishes a general framework for Cu XAS prediction, and can be extended to more computationally expensive levels of theory and to other transition metal L-edges.

cond-mat.mtrl-sci↗

arXiv2024

AuNR-SMA: Automated Gold Nanorod Spectral Morphology Analysis Pipeline

The development of a colloidal synthesis procedure to produce nanomaterials of a specific size with high shape and size purity is often a time consuming, iterative process. This is often due to the time, resource and expertise intensive characterization methods required for quantitative determination of nanomaterial size and shape. Absorption spectroscopy is often the easiest method of colloidal nanomaterial characterization, however, due to the lack of a reliable method to extract nanoparticle shapes from absorption spectroscopy, it is generally treated as a more qualitative measure for metal nanoparticles. This work demonstrates a gold nanorod (AuNR) spectral morphology analysis (SMA) tool, AuNR-SMA, which is a fast and accurate method to extract quantitative information about an AuNR sample's structural parameters from its absorption spectra. We apply AuNR-SMA in three distinct applications. First, we demonstrate its utility as an automated analysis tool in a high throughput AuNR synthesis procedure by generating quantitative size information from optical spectra. Second, we use the predictions generated by this model to train a machine learning model capable of predicting the resulting AuNR size distributions from the reaction conditions used to synthesize them. Third, we turn this model to spectra extracted from the literature where no size distributions are reported to impute unreported quantitative information of AuNR synthesis. This approach can potentially be extended to any other nanocrystal system where the absorption spectra are size dependent and accurate numerical simulation of the absorption spectra is possible. In addition, this pipeline could be integrated into automated synthesis apparatuses to provide interpretable data from simple measurements and help explore the synthesis science of nanoparticles in a rational manner or facilitate closed-loop workflows.

cond-mat.mes-hall↗

arXiv2024

Random Forest Prediction of Crystal Structure from Electron Diffraction Patterns Incorporating Multiple Scattering

Diffraction is the most common method to solve for unknown or partially known crystal structures. However, it remains a challenge to determine the crystal structure of a new material that may have nanoscale size or heterogeneities. Here we train an architecture of hierarchical random forest models capable of predicting the crystal system, space group, and lattice parameters from one or more unknown 2D electron diffraction patterns. Our initial model correctly identifies the crystal system of a simulated electron diffraction pattern from a 20 nm thick specimen of arbitrary orientation 67% of the time. We achieve a topline accuracy of 79% when aggregating predictions from 10 patterns of the same material but different zone axes. The space group and lattice predictions range from 70-90% accuracy and median errors of 0.01-0.5 angstroms, respectively, for cubic, hexagonal, trigonal and tetragonal crystal systems while being less reliable on orthorhombic and monoclinic systems. We apply this architecture to a 4D-STEM scan of gold nanoparticles, where it accurately predicts the crystal structure and lattice constants. These random forest models can be used to significantly accelerate the analysis of electron diffraction patterns, particularly in the case of unknown crystal structures. Additionally, due to the speed of inference, these models could be integrated into live TEM experiments, allowing real-time labeling of a specimen.

cond-mat.mtrl-sci↗

arXiv2023

Prediction of the Cu Oxidation State from EELS and XAS Spectra Using Supervised Machine Learning

Electron energy loss spectroscopy (EELS) and X-ray absorption spectroscopy (XAS) provide detailed information about bonding, distributions and locations of atoms, and their coordination numbers and oxidation states. However, analysis of XAS/EELS data often relies on matching an unknown experimental sample to a series of simulated or experimental standard samples. This limits analysis throughput and the ability to extract quantitative information from a sample. In this work, we have trained a random forest model capable of predicting the oxidation state of copper based on its L-edge spectrum. Our model attains an $R^2$ score of 0.85 and a root mean square valence error of 0.24 on simulated data. It has also successfully predicted experimental L-edge EELS spectra taken in this work and XAS spectra extracted from the literature. We further demonstrate the utility of this model by predicting simulated and experimental spectra of mixed valence samples generated by this work. This model can be integrated into a real time EELS/XAS analysis pipeline on mixtures of copper containing materials of unknown composition and oxidation state. By expanding the training data, this methodology can be extended to data-driven spectral analysis of a broad range of materials.

cond-mat.mtrl-sci↗