arXiv subjects

Rachel C. Kurchin

Publications and source records attributed to Rachel C. Kurchin.

9 recordsLinked to original sources

arXiv2026

Kinetics studies on $κ$ to $β$-Ga$_2$O$_3$ phase transformations via in-situ high temperature X-ray diffraction

The kinetics of the $κ$ to $β$-Ga$_2$O$_3$ phase transformation were investigated in five batches of nominally phase-pure $κ$-Ga2O3 thin films heteroepitaxially grown on c-plane sapphire, with film thickness ranging from 700 to 1100 nm, using in-situ high-temperature X-ray diffraction. Phase fractions were quantitatively extracted through modified Rietveld refinement that accounts for preferred orientation, and the transformation kinetics were analyzed using the Johnson-Mehl-Avrami-Kolmogorov (JMAK) model. The applicability of the JMAK model to thin-film materials was evaluated and its lower and upper bounds for thin films and bulk materials were established. Based on this analysis, a method specifically suited for thin-film kinetic studies was developed and yielded reproducible and robust results across all five sample batches. The results indicate that the $κ$ to $β$ phase transformation in ~700-1100 nm films is best described as an interface-controlled, site-saturated nucleation with thickness-limited or effectively two-dimensional growth.

cond-mat.mtrl-sci↗

arXiv2026

First-Principles Study of Mg-Induced Phase Stabilization in Ga$_2$O$_3$ polymorphs

In this study, we investigate the effect of Mg incorporation on the relative phase stability of the four primary Ga$_2$O$_3$ polymorphs using density functional theory (DFT) calculations, with the goal of rationalizing experimental observations suggesting that diffusion from MgAl$_2$O$_4$ substrates contributes to relative stabilization of the $γ$ phase. Mg incorporation is modeled up to 25% of Ga sites within supercells derived from fully relaxed unit cells of each polymorph. Our results show that while $β$-Ga$_2$O$_3$ remains the thermodynamically most stable phase, the enthalpic differences between polymorphs decrease with increasing Mg content. The inherently disordered $γ$ phase, with its high configurational entropy, becomes less energetically unfavorable under Mg substitution, suggesting that entropy-driven stabilization may facilitate its formation under high-temperature and/or nonequilibrium growth conditions such as those previously reported. These findings provide a thermodynamic rationale for the experimental observation of the $γ$ phase during epitaxial growth on MgAl$_2$O$_4$ spinel substrates.

cond-mat.mtrl-sci↗

arXiv2025

Analysis and Uncertainty Quantification of Thermal Transport Measurements through Bayesian Parameter Estimation

The thermal transport community is increasingly interested in rigorous uncertainty quantification (UQ) of their measurements. In this work, we argue that Bayesian parameter estimation (BPE) represents a powerful framework for both analysis/fitting and UQ. We provide a detailed walkthrough of the technique (including code to duplicate our results) and example analysis based on measuring the thermal conductance of a gold/sapphire interface with FDTR. Comparisons are made against traditional analysis/UQ techniques adopted by the thermal transport community. Notable advantages of BPE include the interpretability of its results, including the capacity to indicate incorrect input assumptions, as well as a way to balance overall goodness of fit against prior knowledge of feasible parameter values. In some cases, incorporating this additional information can affect not only the magnitude of error bars but the inferred values themselves.

cond-mat.mtrl-sci↗

arXiv2023

Generalization of Graph-Based Active Learning Relaxation Strategies Across Materials

Although density functional theory (DFT) has aided in accelerating the discovery of new materials, such calculations are computationally expensive, especially for high-throughput efforts. This has prompted an explosion in exploration of machine learning assisted techniques to improve the computational efficiency of DFT. In this study, we present a comprehensive investigation of the broader application of Finetuna, an active learning framework to accelerate structural relaxation in DFT with prior information from Open Catalyst Project pretrained graph neural networks. We explore the challenges associated with out-of-domain systems: alcohol ($C_{>2}$) on metal surfaces as larger adsorbates, metal-oxides with spin polarization, and three-dimensional (3D) structures like zeolites and metal-organic-frameworks. By pre-training machine learning models on large datasets and fine-tuning the model along the simulation, we demonstrate the framework's ability to conduct relaxations with fewer DFT calculations. Depending on the similarity of the test systems to the training systems, a more conservative querying strategy is applied. Our best-performing Finetuna strategy reduces the number of DFT single-point calculations by 80% for alcohols and 3D structures, and 42% for oxide systems.

cond-mat.mtrl-sci↗

arXiv2023

Chemellia: An Ecosystem for Atomistic Scientific Machine Learning

Chemellia is an open-source framework for atomistic machine learning in the Julia programming language. The framework takes advantage of Julia's high speed as well as the ability to share and reuse code and interfaces through the paradigm of multiple dispatch. Chemellia is designed to make use of existing interfaces and avoid ``reinventing the wheel'' wherever possible. A key aspect of the Chemellia ecosystem is the ChemistryFeaturization interface for defining and encoding features -- it is designed to maximize interoperability between featurization schemes and elements thereof, to maintain provenance of encoded features, and to ensure easy decodability and reconfigurability to enable feature engineering experiments. This embodies the overall design principles of the Chemellia ecosystem: separation of concerns, interoperability, and transparency. We illustrate these principles by discussing the implementation of crystal graph convolutional neural networks for material property prediction.

cs.CE↗

arXiv2022

Nonequilibrium Electrochemical Phase Maps: Beyond Butler-Volmer Kinetics

Electrochemical kinetics at electrode-electrolyte interfaces are crucial to understand high-rate behavior of energy storage devices. Phase transformation of electrodes is typically treated under equilibrium thermodynamic conditions, while realistic operation is at finite rates. Analyzing phase transformations under nonequilibrium conditions requires integrating nonlinear electrochemical kinetic models with thermodynamic models. This had only previously been demonstrated for Butler-Volmer kinetics, where it can be done analytically. In this work, we develop a kinetic modeling package in the Julia language capable of efficient numerical inversion of rate relationships for general kinetic models using automatic differentiation. We demonstrate building nonequilibrium phase maps, including for models such as Marcus-Hush-Chidsey that require computation of an integral, and also discuss the impact of a variety of assumptions and model parameters (such as temperature, reorganization energy, activity, and ideal solution interaction energy), particularly on high-rate phase behavior. Even for a fixed set of parameters, the magnitude of the critical current can vary by in excess of a factor of two amongst kinetic models.

cond-mat.mtrl-sci↗

arXiv2019

How much physics is in a current-voltage curve? Inferring defect properties from photovoltaic device measurements

Defect-assisted recombination processes are critical to understand, as they frequently limit photovoltaic (PV) device performance. However, the physical parameters governing these processes can be extremely challenging to measure, requiring specialized techniques and sample preparation. And yet the fact that they limit performance as measured by current-voltage (JV) characterization indicates that they must have some detectable signal in that measurement. In this work, we use numerical device models that explicitly account for these parameters with high-throughput JV measurements and Bayesian inference to construct probability distributions over recombination parameters, showing the ability to recover values consistent with previously-reported literature measurements. The Bayesian approach enables easy incorporation of data and models from other sources; we demonstrate this with temperature dependence of carrier capture cross-sections. The ability to extract these fundamental physical parameters from standardized, automated measurements on completed devices is promising for both established industrial PV technologies and newer research-stage ones.

physics.app-ph↗

arXiv2018

Bayesim: a tool for adaptive grid model fitting with Bayesian inference

Bayesian inference is a widely used and powerful analytical technique in fields such as astronomy and particle physics but has historically been underutilized in some other disciplines including semiconductor devices. In this work, we introduce Bayesim, a Python package that utilizes adaptive grid sampling to efficiently generate a probability distribution over multiple input parameters to a forward model using a collection of experimental measurements. We discuss the implementation choices made in the code, showcase two examples in photovoltaics, and discuss general prerequisites for the approach to apply to other systems.

physics.data-an↗

arXiv2018

Charge-carrier lifetime measurements in early-stage photovoltaic materials: intuition, uncertainties, and opportunities

Measurements of charge-carrier lifetime in many early-stage thin-film photovoltaic materials can be arduous due to the prevalence of defects and limited information about material properties. In this perspective, we give a brief overview of typical techniques for measuring lifetimes and discuss the intuition involved in estimating lifetimes from such techniques, focusing on time-resolved photoluminescence as an example. We then delve into the underlying assumptions and uncertainties involved in analyzing lifetime measurements. Finally, we outline opportunities for improving accuracy of lifetime measurements by utilizing two emerging techniques to decouple different recombination mechanisms: two-photon spectroscopy, which we demonstrate on BiI3 thin films, and temperature- and injection-dependent current-voltage measurements.

cond-mat.mtrl-sci↗