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Perovskite SrVO3 has recently been proposed as a novel electron emission cathode material. Density functional theory (DFT) calculations suggest multiple low work function surfaces, and recent experimental efforts have consistently demonstrated effective work functions of ∼2.7 eV for polycrystalline samples, both results suggesting, but not directly confirming, that some fraction of even lower work function surface is present. In this work, thermionic electron emission microscopy (ThEEM) and high-field ultraviolet photoemission spectroscopy (UPS) are used to study the local work function distribution and measure the work function of a partially oriented- (110)-SrVO3 perovskite oxide cathode surface. Our results show direct evidence of low work function patches of about 2.0 eV on the cathode surface, with a corresponding onset of observable thermionic emission at 750 °C. We hypothesize that, in our ThEEM and UPS experiments, the high applied electric field suppresses the patch field effect, enabling the direct measurement of local work functions. This measured work function of 2.0 eV is comparable to the previous DFT-calculated work function values of the SrVO-terminated (110) SrVO3 surface (2.3 eV) and SrO-terminated (100) surface (1.9 eV). The measured 2.0 eV value is also much lower than the work function for the (001) LaB6 single crystal cathode (∼2.7 eV) and comparable to the effective work function of B-type dispenser cathodes (∼2.1 eV). If SrVO3 thermionic emitters can be engineered to access domains of this low 2.0 eV work function, they have the potential to significantly improve thermionic emitter-based technologies.

Abstract There has been a growing effort to replace manual extraction of data from research papers with automated data extraction based on natural language processing, language models, and recently, large language models (LLMs). Although these methods enable efficient extraction of data from large sets of research papers, they require a significant amount of up-front effort, expertise, and coding. In this work, we propose the ChatExtract method that can fully automate very accurate data extraction with minimal initial effort and background, using an advanced conversational LLM. ChatExtract consists of a set of engineered prompts applied to a conversational LLM that both identify sentences with data, extract that data, and assure the data’s correctness through a series of follow-up questions. These follow-up questions largely overcome known issues with LLMs providing factually inaccurate responses. ChatExtract can be applied with any conversational LLMs and yields very high quality data extraction. In tests on materials data, we find precision and recall both close to 90% from the best conversational LLMs, like GPT-4. We demonstrate that the exceptional performance is enabled by the information retention in a conversational model combined with purposeful redundancy and introducing uncertainty through follow-up prompts. These results suggest that approaches similar to ChatExtract, due to their simplicity, transferability, and accuracy are likely to become powerful tools for data extraction in the near future. Finally, databases for critical cooling rates of metallic glasses and yield strengths of high entropy alloys are developed using ChatExtract.

An essential aspect of extending safe operation of the world’s active nuclear reactors is understanding and predicting the embrittlement that occurs in the steels that make up the Reactor pressure vessel (RPV). In this work we integrate state of the art machine learning methods using ensembles of neural networks with unprecedented data collection and integration to develop a new model for RPV steel embrittlement. The new model has multiple improvements over previous machine learning and hand-tuned efforts, including greater accuracy (e.g., at high-fluence relevant for extending the life of present reactors), wider domain of applicability (e.g., including a wide-range of compositions), uncertainty quantification, and online accessibility for easy use by the community. These improvements provide a model with significant new capabilities, including the ability to easily and accurately explore compositions, flux, and fluence effects on RPV steel embrittlement for the first time. Furthermore, our detailed comparisons show our approach improves on the leading American Society for Testing and Materials (ASTM) E900-15 standard model for RPV embrittlement on every metric we assessed, demonstrating the efficacy of machine learning approaches for this type of highly demanding materials property prediction.

Residual Cl ligands are found critical to the stability of amorphous TiO2 coatings by atomic layer deposition. Here, in-situ water treatment is developed to remove residual Cl, while preserve its uniform amorphous phase, which improves the lifetime of Si photoanode to 600 h for hydrogen production.

Abstract 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 a database of labeled cavity images which includes 400 images, > 34 k 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 targeted 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.

Abstract Transition metal dichalcogenides (TMDs), especially in two-dimensional (2D) form, exhibit many properties desirable for device applications. However, device performance can be hindered by the presence of defects. Here, we combine state of the art experimental and computational approaches to determine formation energies and charge transition levels of defects in bulk and 2D MX2 (M = Mo or W; X = S, Se, or Te). We perform deep level transient spectroscopy (DLTS) measurements of bulk TMDs. Simultaneously, we calculate formation energies and defect levels of all native point defects, which enable identification of levels observed in DLTS and extend our calculations to vacancies in 2D TMDs, for which DLTS is challenging. We find that reduction of dimensionality of TMDs to 2D has a significant impact on defect properties. This finding may explain differences in optical properties of 2D TMDs synthesized with different methods and lays foundation for future developments of more efficient TMD-based devices.

In laser powder bed fusion processes, keyholes are the gaseous cavities formed where laser interacts with metal, and their morphologies play an important role in defect formation and the final product quality. The in-situ X-ray imaging technique can monitor the keyhole dynamics from the side and capture keyhole shapes in the X-ray image stream. Keyhole shapes in X-ray images are then often labeled by humans for analysis, which increasingly involves attempting to correlate keyhole shapes with defects using machine learning. However, such labeling is tedious, time-consuming, error-prone, and cannot be scaled to large data sets. To use keyhole shapes more readily as the input to machine learning methods, an automatic tool to identify keyhole regions is desirable. In this paper, a deep-learning-based computer vision tool that can automatically segment keyhole shapes out of X-ray images is presented. The pipeline contains a filtering method and an implementation of the BASNet deep learning model to semantically segment the keyhole morphologies out of X-ray images. The presented tool shows promising average accuracy of 91.24% for keyhole area, and 92.81% for boundary shape, for a range of test dataset conditions in Al6061 (and one AliSi10Mg) alloys, with 300 training images/labels and 100 testing images for each trial. Prospective users may apply the presently trained tool or a retrained version following the approach used here to automatically label keyhole shapes in large image sets.

Abstract 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 but that it can be simply calibrated to dramatically improve its accuracy. We demonstrate the effectiveness of this calibration method for both synthetic data and numerous 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.

Abstract Irradiation increases the yield stress and embrittles light water reactor (LWR) pressure vessel steels. In this study, we demonstrate some of the potential benefits and risks of using machine learning models to predict irradiation hardening extrapolated to low flux, high fluence, extended life conditions. The machine learning training data included the Irradiation Variable for lower flux irradiations up to an intermediate fluence, plus the Belgian Reactor 2 and Advanced Test Reactor 1 for very high flux irradiations, up to very high fluence. Notably, the machine learning model predictions for the high fluence, intermediate flux Advanced Test Reactor 2 irradiations are superior to extrapolations of existing hardening models. The successful extrapolations showed that machine learning models are capable of capturing key intermediate flux effects at high fluence. Similar approaches, applied to expanded databases, could be used to predict hardening in LWRs under life-extension conditions.

The evolution of medium range ordering (MRO) and crystallization behavior of amorphous TiO2 films grown by atomic layer deposition (ALD) were studied using in situ four-dimensional scanning transmission electron microscopy. The films remain fully amorphous when grown at 120 °C or below, but they start showing crystallization of anatase phases when grown at 140 °C or above. The degree of MRO increases as a function of temperature and maximizes at 140 °C when crystallization starts to occur, which suggests that crystallization prerequires the development of nanoscale MRO that serves as the site of nucleation. In situ annealing of amorphous TiO2 films grown at 80 °C shows enhancement of MRO but limited number of nucleation, which suggests that post-annealing develops only a small portion of MRO into crystal nuclei. The MRO regions that do not develop into crystals undergo structural relaxation instead, which provides insights into the critical size and degree of ordering and the stability of certain MRO types at different temperatures. In addition, crystallographic defects were observed within crystal phases, which likely negate corrosion resistance of the film. Our result highlights the importance of understanding and controlling MRO for optimizing ALD-grown amorphous films for next-generation functional devices and renewable energy applications.

Materials discovery and design typically proceeds through iterative evaluation (both experimental and computational) to obtain data, generally targeting improvement of one or more properties under one or more constraints (e.g. time or budget). However, there can be great variation in the quality and cost of different data, and when they are mixed together in what we here call multifidelity data, the optimal approaches to their utilization are not established. It is therefore important to develop strategies to acquire and use multifidelity data to realize the most efficient iterative materials exploration. In this work, we assess the impact of using multifidelity data through mock demonstration of designing solar cell materials, using the electronic bandgap as the target property. We propose a new approach of using multifidelity data through leveraging machine learning models of both low- and high-fidelity data, where using predicted low-fidelity data as an input feature in the high-fidelity model can improve the impact of a multifidelity data approach. We show how tradeoffs of low- versus high-fidelity measurement cost and acquisition can impact the materials discovery process. We find that the use of multifidelity data has maximal impact on the materials discovery campaign when approximately five low-fidelity measurements per high-fidelity measurement are performed, and when the cost of low-fidelity measurements is approximately 5% or less than that of high-fidelity measurements. This work provides practical guidance and useful qualitative measures for improving materials discovery campaigns that involve multifidelity data.

The work function is one of the most fundamental surface properties of a material, and understanding and controlling its value is of central importance for manipulating electron flow in applications ranging from high power vacuum electronics to oxide electronics and solar cells. Recent computational studies using Density Functional Theory (DFT) have demonstrated that DFT-calculated work function values for metals tend to agree well (within about 0.3 eV on average) with experimental values. However, a detailed validation of DFT-calculated work functions for oxide materials has not been conducted and is challenging due to the complex dipole structures that can occur on oxide surfaces. In this work, we have focused our investigation on the widely studied perovskite SrTiO3 as a case study example. We find that DFT can accurately predict the work function values of clean and reconstructed SrTiO3 surfaces vs experiment at about the same level of accuracy as metals when direct comparisons can be made. Furthermore, to aid in understanding the factors governing the work function of oxides, we have performed systematic studies on the influence of common surface features, including surface point defects, doping, adsorbates, reconstructions, and surface steps, on the work function. The relationships between the surface structure and work function for SrTiO3 identified here may be qualitatively applicable to other complex oxide materials.

Cu-rich precipitates formation is associated with the precipitation hardening of Fe-Cu based steels and the embrittlement of reactor pressure vessel steels under neutron irradiation. The accurate modeling of the time evolution of Cu-rich precipitates is therefore of fundamental importance for the design of Fe-Cu based steels and the prediction of the irradiation induced shift of the ductile to brittle transition temperature of reactor pressure vessels. This work applies cluster dynamics with mobile Cu monomers and clusters to model Cu precipitation in dilute Fe-Cu alloys at several temperatures. Optimized model parameters can be used to simulate the mean radius, number density, volume fraction, and matrix composition evolution during isothermal annealing with reasonable accuracy. The possible reduction of the mobility of Cu-rich clusters due to additional alloying elements in Fe-Cu based steels is discussed.

The desirable functional properties of complex oxide materials are often influenced by the presence of oxygen defects and epitaxial strain. Meyer et al. demonstrate the role of oxygen defect kinetics in the strain control of the superconducting transition temperature of LSCO.

Degradation of cathode materials is a key factor hindering the long-term stability of lithium ion batteries. Here, the authors develop a high-throughput computational approach to design effective cathode coating materials, proposing a selection of candidate materials to help improve cathode lifetimes.

To better present the machine learning work and the data used, we prepared a supplemental spreadsheet to organize the full training dataset prepared from DFT calculations, the individual elemental properties, the generated element-based descriptors derived from the elements present in each perovskite composition, and lists of the specific features selected and used our machine learning models. We have also provided supplemental information which contains additional details related to our machine learning models which were not provided in the main text (Li et al., In press) [1]. In particular, the supplemental information provides results on training and testing five regression models (using the same data and descriptors as the regression of Ehull in main text) to predict the formation energies of perovskite oxides. We provided source code that trains the machine learning models on the provided training dataset and predicts the stability for the test data.

Density functional theory modeling was performed to determine the effect of humidity and H_{2}/O_{2} gas pressure on the defect chemistry, hydrogen solubility and diffusivity, and on cation transport in tetragonal bulk ZrO_{2}, for the temperature range 400–1200^{∘}C. The main goal of this study is to identify the stable defect complexes and hydrogen-related defect species relevant to bulk cation transport kinetics at various gas pressure, humidity, and temperature conditions, including cation diffusion via a Zr vacancy mechanism [with −4 charge, V_{Zr}(−4)], through an H-substituted Zr defect mechanism [via anH substituted Zr defect with −3 charge, H_{Zr}(−3)], and via formation of fully or partially bound Schottky defect complexes (V_{Zr}-V_{O} and V_{O}-V_{Zr}-V_{O}). At low temperatures (T

The recently discovered phenomena arising from 2D nanomaterials have led to an increased interest in the fabrication of other ultrathin materials from those typically only observed in the bulk. Here, the authors demonstrate the synthesis of micron-sized, single-crystalline ZnO nanosheets via solution based methods.

Materials that exhibit a low work function and therefore easily emit electrons into vacuum form the basis of electronic devices used in applications ranging from satellite communications to thermionic energy conversion. W–Ba–O is the canonical materials system that functions as the thermionic electron emitter commercially used in a range of high-power electron devices. However, the work functions, surface stability, and kinetic characteristics of a polycrystalline W emitter surface are still not well understood or characterized. In this study, we examined the work function and surface stability of the eight lowest index surfaces of the W–Ba–O system using density functional theory methods. We found that under the typical thermionic cathode operating conditions of high temperature and low oxygen partial pressure, the most stable surface adsorbates are Ba–O species with compositions in the range of Ba0.125O–Ba0.25O per surface W atom, with O passivating all dangling W bonds and Ba creating work function-lowering surface dipoles. Wulff construction analysis reveals that the presence of O and Ba significantly alters the surface energetics and changes the proportions of surface facets present under equilibrium conditions. Analysis of previously published data on W sintering kinetics suggests that fine W particles in the size range of 100-500 nm may be at or near equilibrium during cathode synthesis and thus may exhibit surface orientation fractions well described by the calculated Wulff construction.

The structure, function, stability, and many other properties of a protein in a fixed environment are fully specified by its sequence, but in a manner that is difficult to discern. We present a general approach for rapidly mapping sequences directly to their energies on a pre-specified rigid backbone, an important sub-problem in computational protein design and in some methods for protein structure prediction. The cluster expansion (CE) method that we employ can, in principle, be extended to model any computable or measurable protein property directly as a function of sequence. Here we show how CE can be applied to the problem of computational protein design, and use it to derive excellent approximations of physical potentials. The approach provides several attractive advantages. First, following a one-time derivation of a CE expansion, the amount of time necessary to evaluate the energy of a sequence adopting a specified backbone conformation is reduced by a factor of 10(7) compared to standard full-atom methods for the same task. Second, the agreement between two full-atom methods that we tested and their CE sequence-based expressions is very high (root mean square deviation 1.1-4.7 kcal/mol, R2 = 0.7-1.0). Third, the functional form of the CE energy expression is such that individual terms of the expansion have clear physical interpretations. We derived expressions for the energies of three classic protein design targets-a coiled coil, a zinc finger, and a WW domain-as functions of sequence, and examined the most significant terms. Single-residue and residue-pair interactions are sufficient to accurately capture the energetics of the dimeric coiled coil, whereas higher-order contributions are important for the two more globular folds. For the task of designing novel zinc-finger sequences, a CE-derived energy function provides significantly better solutions than a standard design protocol, in comparable computation time. Given these advantages, CE is likely to find many uses in computational structural modeling.

This work demonstrates how databases of diffusion-related properties can be developed from high-throughput ab initio calculations. The formation and migration energies for vacancies of all adequately stable pure elements in both the face-centered cubic (fcc) and hexagonal close packing (hcp) crystal structures were determined using ab initio calculations. For hcp migration, both the basal plane and z -direction nearest-neighbor vacancy hops were considered. Energy barriers were successfully calculated for 49 elements in the fcc structure and 44 elements in the hcp structure. These data were plotted against various elemental properties in order to discover significant correlations. The calculated data show smooth and continuous trends when plotted against Mendeleev numbers. The vacancy formation energies were plotted against cohesive energies to produce linear trends with regressed slopes of 0.317 and 0.323 for the fcc and hcp structures respectively. This result shows the expected increase in vacancy formation energy with stronger bonding. The slope of approximately 0.3, being well below that predicted by a simple fixed bond strength model, is consistent with a reduction in the vacancy formation energy due to many-body effects and relaxation. Vacancy migration barriers are found to increase nearly linearly with increasing stiffness, consistent with the local expansion required to migrate an atom. A simple semi-empirical expression is created to predict the vacancy migration energy from the lattice constant and bulk modulus for fcc systems, yielding estimates with errors of approximately 30%.