5,740 research outputs found

    Using in-situ microLaue diffraction to understand plasticity in MgO

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    The present study investigates the micromechanical modes of deformation in MgO prior to cracking at room temperature. A combination of time resolved white beam Laue diffraction technique and in-situ nano-indentation of large single crystal micropillars provides a unique method to study the operating mechanisms of deformation in this otherwise brittle oxide ceramic. Upon indenting an [100]-oriented MgO micropillar, rotation and streaking of Laue spots were observed. From the streaking of the Laue spots, differential slip on orthogonal {110} slip planes was inferred to take place in adjacent areas under the indent - this was consistent with the results from the transmission electron microscopy studies. Upon cyclic loading of the pillar, subsequent stretching and relaxation of peaks was hypothesised to happen due to pronounced mechanical hysteresis commonly observed in MgO. Also, time-resolved spatial mapping of the deformation gradients of the area under the indent were obtained from which the strain and rotation components were identified

    Using coupled micropillar compression and micro-Laue diffraction to investigate deformation mechanisms in a complex metallic alloy Al13Co4

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    In this investigation, we have used in-situ micro-Laue diffraction combined with micropillar compression of focused ion beam milled Al13Co4 complex metallic alloy to study the evolution of deformation in Al13Co4. Streaking of the Laue spots showed that the onset of plastic flow occured at stresses as low as 0.8 GPa, although macroscopic yield only becomes apparent at 2 GPa. The measured misorientations, obtained from peak splitting, enabled the geometrically necessary dislocation density to be estimated as 1.1 x 1013 m-2

    Interpretable Machine Learning for Materials Design

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    Fueled by the widespread adoption of Machine Learning (ML) and the high-throughput screening of materials, the data-centric approach to materials design has asserted itself as a robust and powerful tool for the in-silico prediction of materials properties. When training models to predict material properties, researchers often face a difficult choice between a model's interpretability or its performance. We study this trade-off by leveraging four different state-of-the-art ML techniques: XGBoost, SISSO, Roost, and TPOT for the prediction of structural and electronic properties of perovskites and 2D materials. We then assess the future outlook of the continued integration of ML into materials discovery and identify key problems that will continue to challenge researchers as the size of the literature's datasets and complexity of models increases. Finally, we offer several possible solutions to these challenges with a focus on retaining interpretability and share our thoughts on magnifying the impact of ML on materials design
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