arXiv subjects

Samantha Daly

Publications and source records attributed to Samantha Daly.

6 recordsLinked to original sources

arXiv2026

Reversible Modulation of Thermal Conductivity in GaN through Strain-Field Screening around Dislocations

Crystalline defects are generally regarded as static phonon scatterers that irreversibly suppress thermal transport. Here we show that elastic strain can reversibly modify dislocation-associated strain fields and strongly alter heat conduction. Using in situ strain-dependent time-domain thermoreflectance measurements, we observe a reversible enhancement of thermal conductivity in GaN by 23% under only 0.21% uniaxial strain. High-resolution X-ray diffraction reveals progressive narrowing of the symmetric (0002) reflection, indicating a reduction in the distribution of lattice rotations and heterogeneous strain. High-resolution electron backscatter diffraction directly shows that the spatial autocorrelations of multiple strain components decay over progressively shorter distances with applied strain, providing real-space evidence for enhanced screening of long-range strain fields. Raman spectroscopy further shows a non-monotonic evolution of the $E_{2}^{\mathrm{high}}$ phonon linewidth near the onset of the thermal-conductivity increase. Together, these results support a picture in which elastic strain reversibly reconfigures pinned dislocation lines and shortens the spatial range of their heterogeneous strain fields, thereby reducing phonon scattering. Our work establishes defect-associated strain correlations as a tunable degree of freedom for controlling thermal transport in crystalline solids.

cond-mat.mtrl-sci↗

arXiv2025

Preference Learning from Physics-Based Feedback: Tuning Language Models to Design BCC/B2 Superalloys

We apply preference learning to the task of language model-guided design of novel structural alloys. In contrast to prior work that focuses on generating stable inorganic crystals, our approach targets the synthesizeability of a specific structural class: BCC/B2 superalloys, an underexplored family of materials with potential applications in extreme environments. Using three open-weight models (LLaMA-3.1, Gemma-2, and OLMo-2), we demonstrate that language models can be optimized for multiple design objectives using a single, unified reward signal through Direct Preference Optimization (DPO). Unlike prior approaches that rely on heuristic or human-in-the-loop feedback (costly), our reward signal is derived from thermodynamic phase calculations, offering a scientifically grounded criterion for model tuning. To our knowledge, this is the first demonstration of preference-tuning a language model using physics-grounded feedback for structural alloy design. The resulting framework is general and extensible, providing a path forward for intelligent design-space exploration across a range of physical science domains.

cs.CE↗

arXiv2025

Toward Reliable Ad-hoc Scientific Information Extraction: A Case Study on Two Materials Datasets

We explore the ability of GPT-4 to perform ad-hoc schema based information extraction from scientific literature. We assess specifically whether it can, with a basic prompting approach, replicate two existing material science datasets, given the manuscripts from which they were originally manually extracted. We employ materials scientists to perform a detailed manual error analysis to assess where the model struggles to faithfully extract the desired information, and draw on their insights to suggest research directions to address this broadly important task.

cs.CL↗

arXiv2025

Integrated Experiment and Simulation Co-Design: A Key Infrastructure for Predictive Mesoscale Materials Modeling

The design of structural & functional materials for specialized applications is being fueled by rapid advancements in materials synthesis, characterization, manufacturing, with sophisticated computational materials modeling frameworks that span a wide spectrum of length & time scales in the mesoscale between atomistic & continuum approaches. This is leading towards a systems-based design methodology that will replace traditional empirical approaches, embracing the principles of the Materials Genome Initiative. However, several gaps remain in this framework as it relates to advanced structural materials:(1) limited availability & access to high-fidelity experimental & computational datasets, (2) lack of co-design of experiments & simulation aimed at computational model validation,(3) lack of on-demand access to verified and validated codes for simulation and for experimental analyses, & (4) limited opportunities for workforce training and educational outreach. These shortcomings stifle major innovations in structural materials design. This paper describes plans for a community-driven research initiative that addresses current gaps based on best-practice recommendations of leaders in mesoscale modeling, experimentation & cyberinfrastructure obtained at an NSF-sponsored workshop dedicated to this topic. The proposal is to create a hub for Mesoscale Experimentation and Simulation co-Operation (hMESO)-that will (I) provide curation and sharing of models, data, & codes, (II) foster co-design of experiments for model validation with systematic uncertainty quantification, & (III) provide a platform for education & workforce development. It will engage experimental & computational experts in mesoscale mechanics and plasticity, along with mathematicians and computer scientists with expertise in algorithms, data science, machine learning, & large-scale cyberinfrastructure initiatives.

cond-mat.mtrl-sci↗

arXiv2024

Advances in high-pressure laser floating zone growth: the Laser Optical Kristallmacher II

The optical floating zone crystal growth technique is a well-established method for obtaining large, high-purity single crystals. While the floating zone method has been constantly evolving for over six decades, the development of high-pressure (up to 1000 bar) growth systems has only recently been realized via the combination of laser-based heating sources with an all-metal chamber. While our inaugural high-pressure laser floating zone furnace design demonstrated the successful growth of new volatile and metastable phases, the furnace design faces several limitations with imaging quality, heating profile control, and chamber cooling power. Here, we present a second-generation design of the high-pressure laser floating zone furnace, "Laser Optical Kristallmacher II" (LOKII), and demonstrate that this redesign facilitates new advances in crystal growth by highlighting several exemplar materials: $α$-Fe$_2$O$_3$, $β$-Ga$_2$O$_3$, and La$_2$CuO$_{4+δ}$. Notably, for La$_2$CuO$_{4+δ}$, we demonstrate the feasibility and long-term stability of traveling solvent floating zone growth under a record pressure of 700 bar.

physics.app-ph↗

arXiv2022

Dark Field X-ray Microscopy Below Liquid-Helium Temperature: The Case of NaMnO2

Dark field X-ray microscopy (DFXM) is an experimental technique employed to investigate material properties by probing their 'mesoscale,' or microscale structures, in a bulk-sensitive manner using hard X-rays at synchrotron radiation sources. However, challenges remain when it comes to applications of this technique to examine low-temperature phenomena in quantum materials, which exhibit complex phase transitions at cryogenic temperatures. One such material is NaMnO2, which hosts an antiferromagnetic transition at 45 K that is suspected to coincide with local structural transitions from its majority monoclinic phase to nanoscale triclinic domains. Direct observation of local heterogeneities and this effect at low temperatures in NaMnO2 is an important step in understanding this material, and serves as an ideal candidate study for expanding the DFXM experimental design space. This paper details a foundational high-resolution DFXM study, down to liquid-helium temperature and below, conducted to explore phase transitions in NaMnO2. The outlined experiment ushers in the evaluation of other functional materials at low temperatures using this technique.

cond-mat.mtrl-sci↗