arXiv subjects

Matthias J. Young

Publications and source records attributed to Matthias J. Young.

7 recordsLinked to original sources

arXiv2026

Fast 4D-STEM-based phase mapping for amorphous and mixed materials

Interpretation and mapping strategies for 4D-scanning transmission electron microscopy (4D-STEM) are well-developed for crystalline materials, yet in the case of amorphous and mixed materials it is significantly more challenging to separate different phases. Non-negative matrix factorization (NMF) in principle would allow separation of 4D-STEM data into components with interpretable diffraction signatures and intensity maps, independent of the crystalline, amorphous or mixed nature of the material. However, adoption of NMF in this field is hampered by large datasets and conceptual hurdles: NMF tackles a non-convex optimization problem, requiring iterative algorithms. Additionally, the stopping condition has to be chosen carefully. In this work, we show that the factorization of large 4D-STEM datasets can be drastically accelerated using a QB decomposition (i.e. randomized NMF or RNMF), leading to much shorter time per iteration. This allows structure-independent phase mapping on very large 4D-STEM datasets. We validate this approach on a synthetic literature dataset (mixed ZrCuAl), before mapping a thin TiO$_2$ layer on top of SiO$_2$, and an interface between a lithium-ion cathode and solid state electrolyte. We also demonstrate that, before using NMF to transform the data on an interpretable, nonnegative basis, principal component analysis (PCA) can be used for fast exploratory analysis to assess dataset dimensionality and linearity.

cond-mat.mtrl-sci↗

arXiv2026

Using superpixels for interpretable feature reduction in large 2D diffraction datasets

Large 2D diffraction datasets, consisting of hundreds or thousands of measurements, are commonly acquired with 4D-STEM electron diffraction or at synchrotron X-ray beamlines. Machine learning and artificial intelligence offer great promise for analyzing these datasets. However, the sheer volume of data presents a significant data processing bottleneck. Cropping the detector and pixel binning are standard ways to reduce data size. Here we propose grouping and averaging pixels into superpixels of variable area. High-information regions are sampled densely, while low-information areas are collected into larger superpixels. In the process, symmetries in the data are captured and exploited, making this approach suitable for preprocessing 2D diffraction data. We compare two variance-minimizing methods: K-means clustering (top-down) and agglomerative clustering (bottom-up) and demonstrate superior scaling and interpretability for the bottom-up method. As these methods are distance-based, we demonstrate that the construction of superpixels can be accelerated using Gaussian random projection. Finally we show over 100-fold acceleration for phase mapping with Non-negative matrix factorization on a 4D-STEM dataset when superpixels are used as a preprocessing step.

cond-mat.mtrl-sci↗

arXiv2026

Data Fusion and Contrastive Alignment for Unconstrained IR Molecular Structure Elucidation

Automated molecular structure elucidation from infrared (IR) spectroscopy data has seen significant advancements in recent years, but its broad applicability is limited by a reliance on pre-determined chemical formulas provided as auxiliary model inputs. This limits model predictions to isomer identification rather than full molecular structure prediction. Although transformer models have been shown to identify molecular isomers with high accuracy, their reliability for unconstrained structure elucidation is comparatively low and poorly understood. In this work, we propose and evaluate key modifications to the traditional encoder-decoder transformer. To better address the vast chemical space of the unconstrained problem, we implement a novel Mixture-of-Experts (MoE) decoder module that utilizes non-additive aggregation via linear-order statistics and the Choquet integral. We further modify the transformer to utilize these non-additive operators when aggregating spectral representations as well. Together with an auxiliary contrastive alignment loss term, these enhancements improve Top-K prediction accuracy by over 10 percentage points compared to baseline IR-only models. Through sub-structure fragment analysis of molecular predictions, we further confirm that infrared spectra encode the vast majority of relevant chemical information, implying that the higher performance of isomer-ranking models is largely due to underrepresented or overlapping absorption bands for molecules in the explored chemical space. Ultimately, by demonstrating the efficacy of automated molecular structure elucidation from measured IR spectra, this work serves to significantly broaden the utility of AI in analytical chemistry.

cs.LG↗

arXiv2026

LLM Agents for Knowledge Discovery in Atomic Layer Processing

Large Language Models (LLMs) have garnered significant attention for several years now. Recently, their use as independently reasoning agents has been proposed. In this work, we test the potential of such agents for knowledge discovery in materials science. We repurpose LangGraph's tool functionality to supply agents with a black box function to interrogate. In contrast to process optimization or performing specific, user-defined tasks, knowledge discovery consists of freely exploring the system, posing and verifying statements about the behavior of this black box, with the sole objective of generating and verifying generalizable statements. We provide proof of concept for this approach through a children's parlor game, demonstrating the role of trial-and-error and persistence in knowledge discovery, and the strong path-dependence of results. We then apply the same strategy to show that LLM agents can explore, discover, and exploit diverse chemical interactions in an advanced Atomic Layer Processing reactor simulation using intentionally limited probe capabilities without explicit instructions.

cs.AI↗

arXiv2025

Active Learning and Explainable AI for Multi-Objective Optimization of Spin Coated Polymers

Spin coating polymer thin films to achieve specific mechanical properties is inherently a multi-objective optimization problem. We present a framework that integrates an active Pareto front learning algorithm (PyePAL) with visualization and explainable AI techniques to optimize processing parameters. PyePAL uses Gaussian process models to predict objective values (hardness and elasticity) from the design variables (spin speed, dilution, and polymer mixture), guiding the adaptive selection of samples toward promising regions of the design space. To enable interpretable insights into the high-dimensional design space, we utilize UMAP (Uniform Manifold Approximation and Projection) for two-dimensional visualization of the Pareto front exploration. Additionally, we incorporate fuzzy linguistic summaries, which translate the learned relationships between process parameters and performance objectives into linguistic statements, thus enhancing the explainability and understanding of the optimization results. Experimental results demonstrate that our method efficiently identifies promising polymer designs, while the visual and linguistic explanations facilitate expert-driven analysis and knowledge discovery.

cs.LG↗

arXiv2021

cryo-ePDF: Overcoming Electron Beam Damage to Study the Local Atomic Structure of Amorphous ALD Aluminum Oxide Thin Films within a TEM

Atomic layer deposition (ALD) provides uniform and conformal thin films that are of interest for a range of applications. To better understand the properties of amorphous ALD films, we need improved understanding of their local atomic structure. Previous work demonstrated measurement of how the local atomic structure of ALD-grown aluminum oxide (AlOx) evolves in operando during growth by employing synchrotron high energy X-ray diffraction (HE-XRD). In this work, we report on efforts to employ electron diffraction pair distribution function (ePDF) measurements using more broadly available transmission electron microscope (TEM) instrumentation to study the atomic structure of amorphous ALD-AlOx. We observe electron beam damage in the ALD-coated samples during ePDF at ambient temperature and successfully mitigate this beam damage using ePDF at cryogenic temperatures (cryo-ePDF). We employ cryo-ePDF and Reverse Monte Carlo (RMC) modeling to obtain structural models of ALD-AlOx coatings formed at a range of deposition temperatures from 150-332°C. From these model structures, we derive structural metrics including stoichiometry, pair distances, and coordination environments in the ALD-AlOx films as a function of deposition temperature. The structural variations we observe with growth temperature are consistent with temperature-dependent changes in the surface hydroxyl density on the growth surface. The sample preparation and cryo-ePDF procedures we report here can be used for routine measurement of ALD-grown amorphous thin films to improve our understanding of the atomic structure of these materials, establish structure-property relationships, and help accelerate the timescale for the application of ALD to address technological needs.

cond-mat.mtrl-sci↗

arXiv2014

Charge Storage in Cation Incorporated α-MnO2

Electrochemical supercapacitors utilizing α-MnO2 offer the possibility of both high power density and high energy density. Unfortunately, the mechanism of electrochemical charge storage in α-MnO2 and the effect of operating conditions on the charge storage mechanism are generally not well understood. Here, we present the first detailed charge storage mechanism of α-MnO2 and explain the capacity differences between α- and β-MnO2 using a combined theoretical electrochemical and band structure analysis. We identify the importance of the band gap, work function, the point of zero charge, and the tunnel sizes of the electrode material, as well as the pH and stability window of the electrolyte in determining the viability of a given electrode material. The high capacity of α-MnO2 results from cation induced charge-switching states in the band gap that overlap with the scanned potential allowed by the electrolyte. The charge-switching states originate from interstitial and substitutional cations (H+, Li+, Na+, and K+) incorporated into the material. Interstitial cations are found to induce charge-switching states by stabilizing Mn-O antibonding orbitals from the conduction band. Substitutional cations interact with O[2p] dangling bonds that are destabilized from the valence band by Mn vacancies to induce charge-switching states. We calculate the equilibrium electrochemical potentials at which these states are reduced and predict the effect of the electrochemical operating conditions on their contribution to charge storage. The mechanism and theoretical approach we report is general and can be used to computationally screen new materials for improved charge storage via ion incorporation.

cond-mat.mtrl-sci↗