arXiv subjects

Wesley Reinhart

Publications and source records attributed to Wesley Reinhart.

5 recordsLinked to original sources

arXiv2026

Modeling High Entropy Alloys' Mechanical Property through Natural Language-Derived Descriptors

Processing treatments of alloys, despite being influential to alloy properties, are often neglected in machine-learning aided alloy designs due to the difficulties in expressing this information. We investigated the expressiveness of transformer embeddings through synthesized annealing processing treatment text and verified that embeddings could be utilized to reconstruct the processing parameters of alloys effectively with an R2>0.99. We then utilized the vector representations of alloys' processing treatment descriptions as descriptors to model high-entropy alloys' hardness and achieved a 20% improvement in prediction, verifying that natural language-derived descriptors of processing treatment information could be utilized to improve prediction of alloy properties.

cond-mat.mtrl-sci↗

arXiv2026

Machine Learning Guided Polymorph Selection in Molecular Beam Epitaxy of In2Se3

Indium selenide (In2Se3), a layered chalcogenide with multiple polymorphs, is a promising material for optoelectronic and ferroelectric applications. However, achieving polymorph-pure thin films remains a major challenge due to the complex growth space. In this work, Bayesian optimization (BO) is successfully leveraged to guide the molecular beam epitaxy growth of In2Se3 on Al2O3 substrates. By training a predictive Gaussian process regressor with sequential learning, we efficiently explored substrate temperature, indium flux, selenium flux, and cracker temperature, reducing experimental trials required for successful synthesis. A {\gamma}-In2Se3 film with 91% phase purity was achieved in fewer than 10 BO run samples. Attempts to isolate {\alpha}-In2Se3 were limited by amorphous film formation at low temperatures, indicating that single-step codeposition is unsuitable for crystalline {\alpha}-In2Se3 on Al2O3. Overall, this study validates BO as a powerful approach for phase-selective growth in complex material systems.

cond-mat.mtrl-sci↗

arXiv2026

Building Envelope Inversion by Data-driven Interpretation of Ground Penetrating Radar

Ground-penetrating radar (GPR) combines depth resolution, non-destructive operation, and broad material sensitivity, yet it has seen limited use in diagnosing building envelopes. The compact geometry of wall assemblies, where reflections from closely spaced studs, sheathing, and cladding strongly overlap, has made systematic inversion difficult. Recent advances in data-driven interpretation provide an opportunity to revisit this challenge and assess whether machine learning can reliably extract structural information from such complex signals. Here, we develop a GPR-based inversion framework that decomposes wall diagnostics into classification tasks addressing vertical (stud presence) and lateral (wall-type) variations. Alongside model development, we implement multiple feature minimization strategies - including recursive elimination, agglomerative clustering, and L0-based sparsity - to promote fidelity and interpretability. Among these approaches, the L0-based sparse neural network (SparseNN) emerges as particularly effective: it exceeds Random Forest accuracy while relying on only a fraction of the input features, each linked to identifiable dielectric interfaces. SHAP analysis further confirms that the SparseNN learns reflection patterns consistent with physical layer boundaries. In summary, this framework establishes a foundation for physically interpretable and data-efficient inversion of wall assemblies using GPR radargrams. Although defect detection is not addressed here, the ability to reconstruct intact envelope structure and isolate features tied to key elements provides a necessary baseline for future inversion and anomaly-analysis tasks.

eess.SP↗

arXiv2025

Expert-Grounded Automatic Prompt Engineering for Extracting Lattice Constants of High-Entropy Alloys from Scientific Publications using Large Language Models

Large language models (LLMs) have shown promise for scientific data extraction from publications, but rely on manual prompt refinement. We present an expert-grounded automatic prompt optimization framework that enhances LLM entity extraction reliability. Using high-entropy alloy lattice constant extraction as a testbed, we optimized prompts for Claude 3.5 Sonnet through feedback cycles on seven expert-annotated publications. Despite a modest optimization budget, recall improved from 0.27 to > 0.9, demonstrating that a small, expert-curated dataset can yield significant improvements. The approach was applied to extract lattice constants from 2,267 publications, yielding data for 1,861 compositions. The optimized prompt transferred effectively to newer models: Claude 4.5 Sonnet, GPT-5, and Gemini 2.5 Flash. Analysis revealed three categories of LLM mistakes: contextual hallucination, semantic misinterpretation, and unit conversion errors, emphasizing the need for validation protocols. These results establish feedback-guided prompt optimization as a low-cost, transferable methodology for reliable scientific data extraction, providing a scalable pathway for complex LLM-assisted research tasks.

cs.DL↗

arXiv2021

$\mathbb{T}$-Operator Limits on Optical Communication: Metaoptics, Computation, and Input-Output Transformations

We present an optimization framework based on Lagrange duality and the scattering $\mathbb{T}$ operator of electromagnetism to construct limits on the possible features that may be imparted to a collection of output fields from a collection of input fields, i.e., constraints on achievable optical transformations and the characteristics of structured materials as communication channels. Implications of these bounds on the performance of representative optical devices having multi-wavelength or multiport functionalities are examined in the context of electromagnetic shielding, focusing, near-field resolution, and linear computing.

physics.optics↗