arXiv subjects

Ryan Jacobs

Publications and source records attributed to Ryan Jacobs.

At least 37 records · Page 2Linked to original sources

arXiv2022

Materials Swelling Revealed Through Automated Semantic Segmentation of Cavities in Electron Microscopy Images

Accurately quantifying swelling of alloys that have undergone irradiation is essential for understanding alloy performance in a nuclear reactor and critical for the safe and reliable operation of reactor facilities. However, typical practice is for radiation-induced defects in electron microscopy images of alloys to be manually quantified by domain-expert researchers. Here, we employ an end-to-end deep learning approach using the Mask Regional Convolutional Neural Network (Mask R-CNN) model to detect and quantify nanoscale cavities in irradiated alloys. We have assembled the largest database of labeled cavity images to date, which includes 400 images, >34k discrete cavities, and numerous alloy compositions and irradiation conditions. We have evaluated both statistical (precision, recall, and F1 scores) and materials property-centric (cavity size, density, and swelling) metrics of model performance, and performed in-depth analysis of materials swelling assessments. We find our model gives assessments of material swelling with an average (standard deviation) swelling mean absolute error based on random leave-out cross-validation of 0.30 (0.03) percent swelling. This result demonstrates our approach can accurately provide swelling metrics on a per-image and per-condition basis, which can provide helpful insight into material design (e.g., alloy refinement) and impact of service conditions (e.g., temperature, irradiation dose) on swelling. Finally, we find there are cases of test images with poor statistical metrics, but small errors in swelling, pointing to the need for moving beyond traditional classification-based metrics to evaluate object detection models in the context of materials domain applications.

cond-mat.mtrl-sci↗

arXiv2022

Machine learning for impurity charge-state transition levels in semiconductors from elemental properties using multi-fidelity datasets

Quantifying charge-state transition energy levels of impurities in semiconductors is critical to understanding and engineering their optoelectronic properties for applications ranging from solar photovoltaics to infrared lasers. While these transition levels can be measured and calculated accurately, such efforts are time-consuming and more rapid prediction methods would be beneficial. Here, we significantly reduce the time typically required to predict impurity transition levels using multi-fidelity datasets and a machine learning approach employing features based on elemental properties and impurity positions. We use transition levels obtained from low-fidelity (i.e., local-density approximation or generalized gradient approximation) density functional theory (DFT) calculations, corrected using a recently proposed modified band alignment scheme, which well-approximates transition levels from high-fidelity DFT (i.e., hybrid HSE06). The model fit to the large multi-fidelity database shows improved accuracy compared to the models trained on the more limited high-fidelity values. Crucially, in our approach, when using the multi-fidelity data, high-fidelity values are not required for model training, significantly reducing the computational cost required for training the model. Our machine learning model of transition levels has a root mean squared (mean absolute) error of 0.36 (0.27) eV vs high-fidelity hybrid functional values when averaged over 14 semiconductor systems from the II-VI and III-V families. As a guide for use on other systems, we assessed the model on simulated data to show the expected accuracy level as a function of bandgap for new materials of interest. Finally, we use the model to predict a complete space of impurity charge-state transition levels in all zinc blende III-V and II-VI systems.

cond-mat.mtrl-sci↗

arXiv2022

Physical factors governing the shape of the Miram curve knee in thermionic emission

In a current density versus temperature (J-T) (Miram) curve in thermionic electron emission, experimental measurements demonstrate there is a smooth transition between the exponential region and the saturated emission regions, which is sometimes referred to as the "roll-off" or "Miram curve knee". The shape of the Miram curve knee is an important figure of merit for thermionic vacuum cathodes. Specifically, cathodes with a sharp Miram curve knee at low temperature with a flat saturated emission current are typically preferred. Our previous work on modeling nonuniform thermionic emission revealed that the space charge effect and patch field effect are key pieces of physics which impact the shape of the Miram curve knee. This work provides a more complete understanding of the physical factors connecting these physical effects and their relative impact on the shape of the knee, including the smoothness, the temperature, and the flatness of the saturated emission current density. For our analyses, we use a periodic, equal-width striped ("zebra crossing") work function distribution as a model system and illustrate how the space charge and patch field effects restrict the emission current density near the Miram curve knee. The results indicate there are three main physical parameters which significantly impact the shape of the Miram curve. Such physical knowledge directly connects the patch size, work function values, anode-cathode voltage, and anode-cathode gap distance to the shape of the Miram curve, providing new understanding and a guide to the design of thermionic cathodes used as electron sources in vacuum electronic devices (VEDs).

physics.app-ph↗

arXiv2021

Modified Band Alignment Method to Obtain Hybrid Functional Accuracy from Standard DFT: Application to Defects in Highly Mismatched III-V:Bi Alloys

This paper provides an accurate theoretical defect energy database for pure and Bi-containing III-V (III-V:Bi) materials and investigates efficient methods for high-throughput defect calculations based on corrections of results obtained with local and semi-local functionals. Point defects as well as nearest-neighbor and second-nearest-neighbor pair defects were investigated in charge states ranging from -5 to 5. Ga-V:Bi systems (GaP:Bi, GaAs:Bi, and GaSb:Bi) were thoroughly investigated with significantly slower, higher fidelity hybrid Heyd-Scuseria-Ernzerhof (HSE) and significantly faster, lower fidelity local density approximation (LDA) calculations. In both approaches spurious electrostatic interactions were corrected with the Freysoldt correction. The results were verified against available experimental results and used to assess the accuracy of a previous band alignment correction. Here, a modified band alignment method is proposed in order to better predict the HSE values from the LDA ones. The proposed method allows prediction of defect energies with values that approximate those from the HSE functional at the computational cost of LDA (about 20x faster for the systems studied here). Tests of selected point defects in In-V:Bi materials resulted in corrected LDA values having a mean absolute error (MAE)=0.175 eV for defect levels vs. HSE. The method was further verified on an external database of defects and impurities in CdX (X=S, Se, Te) systems, yielding a MAE=0.194 eV. These tests demonstrate the correction to be sufficient for qualitative and semi-quantitative predictions, and may suggest transferability to many semiconductor systems without significant loss in accuracy. Properties of the remaining In-V:Bi defects and all Al-V:Bi defects were predicted with the use of the modified band alignment method.

cond-mat.mtrl-sci↗

arXiv2021

Physics-based Model for Nonuniform Thermionic Electron Emission from Polycrystalline Cathodes

Experimental observations of thermionic electron emission demonstrate a smooth transition between TL and FSCL regions of the emitted-current-density-versus-temperature (J-T) (Miram) curve and the emitted-current-density-versus-voltage (J-V) curve. Knowledge of the temperature and shape of the TL-FSCL transition is important in evaluating the thermionic electron emission performance of cathodes, including predicting the lifetime. However, there have been no first-principles physics-based models that can predict the smooth TL-FSCL transition region for real thermionic cathodes without applying physically difficult to justify a priori assumptions or empirical phenomenological equations. Previous work detailing the nonuniform thermionic emission found that the effects of 3-D space charge, patch fields, and Schottky barrier lowering can lead to a smooth TL-FSCL transition region from a model thermionic cathode surface with a checkerboard spatial distribution of work function values. In this work, we construct a physics-based nonuniform emission model for commercial dispenser cathodes for the first time. This emission model is obtained by incorporating the cathode surface grain orientation via electron backscatter diffraction (EBSD) and the facet-orientation-specific work function values from density functional theory (DFT) calculations. The model enables construction of two-dimensional emitted current density maps of the cathode surface and corresponding J-T and J-V curves. The predicted emission curves show excellent agreement with experiment, not only in TL and FSCL regions but, crucially, also in the TL-FSCL transition region. This model improves the understanding on the relationship between thermionic emission and cathode microstructure, which is beneficial to the design of vacuum electronic devices.

physics.app-ph↗

arXiv2021

Performance, Successes and Limitations of Deep Learning Semantic Segmentation of Multiple Defects in Transmission Electron Micrographs

In this work, we perform semantic segmentation of multiple defect types in electron microscopy images of irradiated FeCrAl alloys using a deep learning Mask Regional Convolutional Neural Network (Mask R-CNN) model. We conduct an in-depth analysis of key model performance statistics, with a focus on quantities such as predicted distributions of defect shapes, defect sizes, and defect areal densities relevant to informing modeling and understanding of irradiated Fe-based materials properties. To better understand the performance and present limitations of the model, we provide examples of useful evaluation tests which include a suite of random splits, and dataset size-dependent and domain-targeted cross validation tests. Overall, we find that the current model is a fast, effective tool for automatically characterizing and quantifying multiple defect types in microscopy images, with a level of accuracy on par with human domain expert labelers. More specifically, the model can achieve average defect identification F1 scores as high as 0.8, and, based on random cross validation, have low overall average (+/- standard deviation) defect size and density percentage errors of 7.3 (+/- 3.8)% and 12.7 (+/- 5.3)%, respectively. Further, our model predicts the expected material hardening to within 10-20 MPa (about 10% of total hardening), which is about the same error level as experiments. Our targeted evaluation tests also suggest the best path toward improving future models is not expanding existing databases with more labeled images but instead data additions that target weak points of the model domain, such as images from different microscopes, imaging conditions, irradiation environments, and alloy types. Finally, we discuss the first phase of an effort to provide an easy-to-use, open-source object detection tool to the broader community for identifying defects in new images.

cs.CV↗

arXiv2021

Work Function Trends and New Low Work Function Boride and Nitride Materials for Electron Emission Applications

LaB6 has been used as a commercial electron emitter for decades. Despite the large number of studies on the work function of LaB6, there is no comprehensive understanding of work function trends in the hexaboride materials family. In this study, we use Density Functional Theory (DFT) calculations to calculated trends of rare earth hexaboride work function and rationalize these trends based on the electronegativity of the metal element. We predict that alloying LaB6 with Ba can further lower the work function by ~0.2 eV. Interestingly, we find that alloyed (La, Ba)B6 can have lower work functions than either LaB6 or BaB6, benefitting from an enhanced surface dipole due to metal element size mismatch. In addition to hexaborides we also investigate work function trends of similar materials families, namely tetraborides and transition metal nitrides, which, like hexaborides, are electrically conductive and refractory and thus may also be promising materials for electron emission applications. We find that tetraborides consistently have higher work functions than their hexaboride analogues as the tetraborides having less ionic bonding and smaller positive surface dipoles. Finally, we find that HfN has a low work function of about 2.2 eV, making HfN a potentially promising new electron emitter material.

cond-mat.mtrl-sci↗

arXiv2021

Discovery and Engineering of Low Work Function Perovskite Materials

Materials with low work functions are critical for an array of applications requiring the facile removal or efficient transport of electrons through a device. Perovskite oxides are a promising class of materials for finding low work functions, and here we target applications in thermionic and field electron emission. Perovskites have highly malleable compositions which enable tunable work function values over a wide range, robust stability at high temperatures, and high electronic conductivities. In this work, we screened over 2900 perovskite oxides in search of stable, conductive, low-work-function materials using Density Functional Theory (DFT) methods. Our work provides insight into the materials chemistry governing the work function value of a perovskite, where materials with barely filled d bands possess the lowest work functions. Our screening has resulted in a total of seven promising compounds, such as BaMoO3 and SrNb0.75Co0.25O3 with work functions of 1.1 eV and 1.5 eV, respectively. These promising materials and others presented in this study may find use as low work function electron emitters in high power vacuum electronic and thermionic energy conversion devices. Moreover, the database of calculated work functions and materials chemistry trends governing the value of the work function may aid in the engineering of perovskite heterojunction devices.

cond-mat.mtrl-sci↗

arXiv2021

Solid phase epitaxial growth of the correlated-electron transparent conducting oxide SrVO3

SrVO3 thin films with a high figure of merit for applications as transparent conductors were crystallized from amorphous layers using solid phase epitaxy (SPE). Epitaxial SrVO3 films crystallized on SrTiO3 using SPE exhibit a room temperature resistivity of 2.5 x 10-5 Ohms cm, a residual resistivity ratio of 3.8, and visible light transmission above 0.5 for a 60 nm-thick film. SrVO3 layers were deposited at room temperature using radio-frequency sputtering in an amorphous form and subsequently crystallized by heating in controlled gas environment. The lattice parameters and mosaic angular width of x-ray reflections from the crystallized films are consistent with partial relaxation of the strain resulting from the epitaxial mismatch between SrVO3 and SrTiO3. A reflection high-energy electron diffraction study of the kinetics of SPE indicates that crystallization occurs via the thermally activated propagation of the crystalline/amorphous interface, similar to SPE phenomena in other perovskite oxides. Thermodynamic calculations based on density functional theory predict the temperature and oxygen partial pressure conditions required to produce the SrVO3 phase and are consistent with the experiments. The separate control of deposition and crystallization conditions in SPE presents new possibilities for the crystallization of transparent conductors in complex geometries and over large areas.

cond-mat.mtrl-sci↗

arXiv2021

Impact of Nonuniform Thermionic Emission on the Transition Behavior between Temperature- and Space-Charge-Limited Emission

Experimental observations have long-established that there exists a smooth roll-off or knee transition between the temperature-limited (TL) and full-space-charge-limited (FSCL) emission regions of the emission current density-temperature J-T (Miram) curve, or the emission current density-voltage J-V curve for a thermionic emission cathode. In this paper, we demonstrate that this experimentally observed smooth transition does not require frequently used a priori assumptions of a continuous distribution of work functions on the cathode surface. Instead, we find the smooth transition arises as a natural consequence of the physics of nonuniform thermionic emission from a spatially heterogeneous cathode surface. We obtain this smooth transition for both J-T and J-V curves using a predictive nonuniform thermionic emission model that includes 3-D space charge, patch fields (electrostatic potential nonuniformity on the cathode surface based on local work function values), and Schottky barrier lowering physics and illustrate that a smooth knee can arise from a thermionic cathode surface with as few as two discrete work function values. Importantly, we find that the inclusion of patch field effects is crucial for obtaining accurate J-T and J-V curves, and the further inclusion of Schottky barrier lowering is needed for accurate J-V curves. This finding, and the emission model provided in this paper have important implications for modeling electron emission from realistic, heterogeneous surfaces. Such modeling is important for improved understanding of the interplay of emission physics, cathode materials engineering, and design of numerous devices employing electron emission cathodes.

physics.app-ph↗

arXiv2021

Calibrated bootstrap for uncertainty quantification in regression models

Obtaining accurate estimates of machine learning model uncertainties on newly predicted data is essential for understanding the accuracy of the model and whether its predictions can be trusted. A common approach to such uncertainty quantification is to estimate the variance from an ensemble of models, which are often generated by the generally applicable bootstrap method. In this work, we demonstrate that the direct bootstrap ensemble standard deviation is not an accurate estimate of uncertainty and propose a calibration method to dramatically improve its accuracy. We demonstrate the effectiveness of this calibration method for both synthetic data and physical datasets from the field of Materials Science and Engineering. The approach is motivated by applications in physical and biological science but is quite general and should be applicable for uncertainty quantification in a wide range of machine learning regression models.

cond-mat.mtrl-sci↗

arXiv2020

Semi-adsorption-controlled growth window for half Heusler FeVSb epitaxial films

The electronic, magnetic, thermoelectric, and topological properties of Heusler compounds (composition $XYZ$ or $X_2 YZ$) are highly sensitive to stoichiometry and defects. Here we establish the existence and experimentally map the bounds of a \textit{semi} adsorption-controlled growth window for semiconducting half Heusler FeVSb films, grown by molecular beam epitaxy (MBE). We show that due to the high volatility of Sb, the Sb stoichiometry is self-limiting for a finite range of growth temperatures and Sb fluxes, similar to the growth of III-V semiconductors such as GaSb and GaAs. Films grown within this window are nearly structurally indistinguishable by X-ray diffraction (XRD) and reflection high energy electron diffraction (RHEED). The highest electron mobility and lowest background carrier density are obtained towards the Sb-rich bound of the window, suggesting that Sb-vacancies may be a common defect. Similar \textit{semi} adsorption-controlled bounds are expected for other ternary intermetallics that contain a volatile species $Z=$\{Sb, As, Bi\}, e.g., CoTiSb, LuPtSb, GdPtBi, and NiMnSb. However, outstanding challenges remain in controlling the remaining Fe/V ($X/Y$) transition metal stoichiometry.

cond-mat.mtrl-sci↗

arXiv2020

Opportunities and Challenges for Machine Learning in Materials Science

Advances in machine learning have impacted myriad areas of materials science, ranging from the discovery of novel materials to the improvement of molecular simulations, with likely many more important developments to come. Given the rapid changes in this field, it is challenging to understand both the breadth of opportunities as well as best practices for their use. In this review, we address aspects of both problems by providing an overview of the areas where machine learning has recently had significant impact in materials science, and then provide a more detailed discussion on determining the accuracy and domain of applicability of some common types of machine learning models. Finally, we discuss some opportunities and challenges for the materials community to fully utilize the capabilities of machine learning.

cond-mat.mtrl-sci↗

arXiv2019

The Materials Simulation Toolkit for Machine Learning (MAST-ML): an automated open source toolkit to accelerate data-driven materials research

As data science and machine learning methods are taking on an increasingly important role in the materials research community, there is a need for the development of machine learning software tools that are easy to use (even for nonexperts with no programming ability), provide flexible access to the most important algorithms, and codify best practices of machine learning model development and evaluation. Here, we introduce the Materials Simulation Toolkit for Machine Learning (MAST-ML), an open source Python-based software package designed to broaden and accelerate the use of machine learning in materials science research. MAST-ML provides predefined routines for many input setup, model fitting, and post-analysis tasks, as well as a simple structure for executing a multi-step machine learning model workflow. In this paper, we describe how MAST-ML is used to streamline and accelerate the execution of machine learning problems. We walk through how to acquire and run MAST-ML, demonstrate how to execute different components of a supervised machine learning workflow via a customized input file, and showcase a number of features and analyses conducted automatically during a MAST-ML run. Further, we demonstrate the utility of MAST-ML by showcasing examples of recent materials informatics studies which used MAST-ML to formulate and evaluate various machine learning models for an array of materials applications. Finally, we lay out a vision of how MAST-ML, together with complementary software packages and emerging cyberinfrastructure, can advance the rapidly growing field of materials informatics, with a focus on producing machine learning models easily, reproducibly, and in a manner that facilitates model evolution and improvement in the future.

physics.comp-ph↗

arXiv2019

Exploring effective charge in electromigration using machine learning

The effective charge of an element is a parameter characterizing the electromgration effect, which can determine the reliability of interconnection in electronic technologies. In this work, machine learning approaches were employed to model the effective charge (z*) as a linear function of physically meaningful elemental properties. Average 5-fold (leave-out-alloy-group) cross-validation yielded root-mean-square-error divided by whole data set standard deviation (RMSE/$σ$) values of 0.37 $\pm$ 0.01 (0.22 $\pm$ 0.18), respectively, and $R^2$ values of 0.86. Extrapolation to z* of totally new alloys showed limited but potentially useful predictive ability. The model was used in predicting z* for technologically relevant host-impurity pairs.

cond-mat.mtrl-sci↗

arXiv2019

Materials Discovery of Stable and Nontoxic Halide Perovskite Materials for High-Efficiency Solar Cells

Two critical limitations of organic-inorganic lead halide perovskite materials for solar cells are their poor stability in humid environments and inclusion of toxic lead. In this study, high-throughput density functional theory (DFT) methods are used to computationally model and screen 1845 halide perovskites in search of new materials without these limitations that are promising for solar cell applications. This study focuses on finding materials that are comprised of nontoxic elements, stable in a humid operating environment, and have an optimal bandgap for one of single junction, tandem Si-perovskite, or quantum dot-based solar cells. Single junction materials are also screened on predicted single junction photovoltaic (PV) efficiencies exceeding 22.7%, which is the current highest reported PV efficiency for halide perovskites. Generally, these methods qualitatively reproduce the properties of known promising nontoxic halide perovskites that have either been experimentally evaluated or predicted from theory. From a set of 1845 materials, 15 materials pass all screening criteria for single junction cell applications, 13 of which have not been previously investigated, such as (CH3NH3)0.75Cs0.25SnI3, ((NH2)2CH)Ag0.5Sb0.5Br3, CsMn0.875Fe0.125I3, ((CH3)2NH2)Ag0.5Bi0.5I3, and ((NH2)2CH)0.5Rb0.5SnI3. These materials, together with others predicted in this study, may be promising candidate materials for stable, highly efficient, and non-toxic perovskite-based solar cells.

cond-mat.mtrl-sci↗

arXiv2019

Assessing correlations of perovskite catalytic performance with electronic structure descriptors

Electronic structure descriptors are computationally efficient quantities used to construct qualitative correlations for a variety of properties. In particular, the oxygen p-band center has been used to guide material discovery and fundamental understanding of an array of perovskite compounds for use in catalyzing the oxygen reduction and evolution reactions. However, an assessment of the effectiveness of the oxygen p-band center at predicting key measures of perovskite catalytic activity has not been made, and would be highly beneficial to guide future predictions and codify best practices. Here, we have used Density Functional Theory at the PBE, PBEsol, PBE+U, SCAN and HSE06 levels to assess the correlations of numerous measures of catalytic performance for a series of technologically relevant perovskite oxides, using the bulk oxygen p-band center as an electronic structure descriptor. We have analyzed correlations of the calculated oxygen p-band center for all considered functionals with the experimentally measured X-ray emission spectroscopy oxygen p-band center and multiple measures of catalytic activity, including high temperature oxygen reduction surface exchange rates, aqueous oxygen evolution current densities, and binding energies of oxygen evolution intermediate species. Our results show that the best correlations for all measures of catalytic activity considered here are made with PBE-level calculations, with strong observed linear correlations with the bulk oxygen p-band center (R2 = 0.81-0.87). This study shows that strong linear correlations between numerous important measures of catalytic activity and the oxygen p-band bulk descriptor can be obtained under a consistent computational framework, and these correlations can serve as a guide for future experiments and simulations for development of perovskite and related oxide catalysts.

cond-mat.mtrl-sci↗

arXiv2019

Material Discovery and Design Principles for Stable, High Activity Perovskite Cathodes for Solid Oxide Fuel Cells

Critical to the development of improved solid oxide fuel cell (SOFC) technology are novel compounds with high oxygen reduction reaction (ORR) catalytic activity and robust stability under cathode operating conditions. Approximately 2145 distinct perovskite compositions are screened for potential use as high activity, stable SOFC cathodes, and it is verified that the screening methodology qualitatively reproduces the experimental activity, stability, and conduction properties of well-studied cathode materials. The calculated oxygen p-band center is used as a first principle-based descriptor of the surface exchange coefficient (k*), which in turn correlates with cathode ORR activity. Convex hull analysis is used under operating conditions in the presence of oxygen, hydrogen, and water vapor to determine thermodynamic stability. This search has yielded 52 potential cathode materials with good predicted stability in typical SOFC operating conditions and predicted k* on par with leading ORR perovskite catalysts. The established trends in predicted k* and stability are used to suggest methods of improving the performance of known promising compounds. The material design strategies and new materials discovered in the computational search help enable the development of high activity, stable compounds for use in future solid oxide fuel cells and related applications.

cond-mat.mtrl-sci↗