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  1. Free, publicly-accessible full text available April 18, 2024
  2. 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.

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  3. 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.

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  4. Advances in machine learning have impacted myriad areas of materials science, such as the discovery of novel materials and 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 and the best practices for their use. In this review, we address aspects of both problems by providing an overview of the areas in which machine learning has recently had significant impact in materials science, and then we 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. 
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  5. null (Ed.)
  6. Abstract

    Engineering a material's work function is of central importance for many technologies and in particular electron emitters used in high‐power vacuum electronics and thermionic energy converters. A low work function surface is typically achieved through unstable surface functional species, especially in high power thermionic electron emitter applications. Discovering and engineering new materials with intrinsic, stable low work functions obtainable without volatile surface species would mark a definitive advancement in the design of electron emitters. This work reports evidence for the existence of a low work function surface on a bulk, monolithic, electrically conductive perovskite oxide: SrVO3. After considering the patch field effect on the heterogeneous emitting surface of the bulk polycrystalline samples, this study suggests the presence of low work function (≈2 eV) emissive grains on SrVO3surface. Emission current densities of 10–100 mA cm–2at ≈1000 °C, comparable to commercial LaB6thermionic cathodes, indicative of an overall effective thermionic work function of 2.3–2.7 eV are obtained. This study demonstrates that perovskites like SrVO3may have intrinsically low work functions comparable to commercialized W‐based dispenser cathodes and suggests that, with further engineering, perovskites may represent a new class of low work function electron emitters.

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