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| 1 | New frontiers for the materials genome initiative显示文摘The Materials Genome Initiative(MGI)advanced a new paradigm for materials discovery and design,namely that the pace of new materials deployment could be accelerated through complementary efforts in theory,computation,and experiment.Along with numerous successes,new challenges are inviting researchers to refocus the efforts and approaches that were originally inspired by the MGI.In May 2017,the National Science Foundation sponsored the workshop“Advancing and Accelerating Materials Innovation Through the Synergistic Interaction among Computation,Experiment,and Theory:Opening New Frontiers”to review accomplishments that emerged from investments in science and infrastructure under the MGI,identify scientific opportunities in this new environment,examine how to effectively utilize new materials innovation infrastructure,and discuss challenges in achieving accelerated materials research through the seamless integration of experiment,computation,and theory.This article summarizes key findings from the workshop and provides perspectives that aim to guide the direction of future materials research and its translation into societal impacts. | Juan J.de Pablo Nicholas E.Jackson Michael A.Webb Long-Qing Chen Joel E.Moore Dane Morgan Ryan Jacobs Tresa Pollock Darrell G.Schlom Eric S.Toberer James Analytis Ismaila Dabo Dean M.DeLongchamp Gregory A.Fiete Gregory M.Grason Geoffroy Hautier Yifei Mo Krishna Rajan Evan J.Reed Efrain Rodriguez Vladan Stevanovic Jin Suntivich Katsuyo Thornton Ji-Cheng Zhao | 2019 | npj Computational Materials2019，，1： | 7 |
| 2 | Automated defect analysis in electron microscopic images显示文摘Electron microscopy and defect analysis are a cornerstone of materials science,as they offer detailed insights on the microstructure and performance of a wide range of materials and material systems.Building a robust and flexible platform for automated defect recognition and classification in electron microscopy will result in the completion of analysis orders of magnitude faster after images are recorded,or even online during image acquisition.Automated analysis has the potential to be significantly more efficient,accurate,and repeatable than human analysis,and it can scale with the increasingly important methods of automated data generation.Herein,an automated recognition tool is developed based on a computer vison–based approach;it sequentially applies a cascade object detector,convolutional neural network,and local image analysis methods.We demonstrate that the automated tool performs as well as or better than manual human detection in terms of recall and precision and achieves quantitative image/defect analysis metrics close to the human average.The proposed approach works for images of varying contrast,brightness,and magnification.These promising results suggest that this and similar approaches are worth exploring for detecting multiple defect types and have the potential to locate,classify,and measure quantitative features for a range of defect types,materials,and electron microscopic techniques. | Wei Li Kevin G.Field Dane Morgan | 2018 | npj Computational Materials2018，，1： | 5 |
| 3 | Machine learning predictions of irradiation embrittlement in reactor pressure vessel steels显示文摘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. | Yu-chen Liu Henry Wu Tam Mayeshiba Benjamin Afflerbach Ryan Jacobs Josh Perry Jerit George Josh Cordell Jinyu Xia Hao Yuan Aren Lorenson Haotian Wu Matthew Parker Fenil Doshi Alexander Politowicz Linda Xiao Dane Morgan Peter Wells Nathan Almirall Takuya Yamamoto G.Robert Odette | 2022 | npj Computational Materials2022，，1： | 0 |
| 4 | Calibration after bootstrap for accurate uncertainty quantification in regression models显示文摘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. | Glenn Palmer Siqi Du Alexander Politowicz Joshua Paul Emory Xiyu Yang Anupraas Gautam Grishma Gupta Zhelong Li Ryan Jacobs Dane Morgan | 2022 | npj Computational Materials2022，，1： | 0 |
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