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Free, publicly-accessible full text available March 22, 2023
The development of structural materials with outstanding mechanical response has long been sought for innumerable industrial, technological, and even biomedical applications. However, these compounds tend to derive their fascinating properties from a myriad of interactions spanning multiple scales, from localized chemical bonding to macroscopic interactions between grains. This diversity has limited the ability of researchers to develop new materials on a reasonable timeline. Fortunately, the advent of machine learning in materials science has provided a new approach to analyze high-dimensional space and identify correlations among the structure-composition-property-processing relationships that may have been previously missed. In this review, we examine some successful examples of using data science to improve known structural materials by analyzing fatigue and failure, and we discuss approaches to develop entirely new classes of structural materials in complex composition spaces including high-entropy alloys and bulk metallic glasses. Highlighting the recent advancement in this field demonstrates the power of data-driven methodologies that will hopefully lead to the production of market-ready structural materials.
Lattice strain and texture analysis of superhard Mo 0.9 W 1.1 BC and ReWC 0.8 via diamond anvil cell deformationMo 0.9 W 1.1 BC and ReWC 0.8 compositions have recently been identified to have exceptional hardness and incompressibility. In this work, these compositions are analyzed via in situ radial X-ray diffraction experiments to comparatively assess lattice strain and texture development. Traditionally, Earth scientists have employed these experiments to enhance understanding of dynamic activity within the deep Earth. However, nonhydrostatic compression experiments provide insight into materials with exceptional mechanical properties, as they help elucidate correlations between structural, elastic, and mechanical properties. Here, analysis of differential strain ( t / G ) and lattice preferred orientation in Mo 0.9 W 1.1 BC suggests that dislocation glide occurs along the (010) plane in orthorhombic Mo 0.9 W 1.1 BC. The (200) and (002) planes support the highest differential strain, while planes which bisect two or three axes, such as the (110) or (191), exhibit relatively lower differential strain. In ReWC 0.8 , which crystallizes in a cubic NaCl-type structure, planar density is correlated to orientation-dependent lattice strain as the low-density (311) plane elastically supports more differential strain than the denser (111), (200), and (220) planes. Furthermore, results indicate that ReWC 0.8 likely supports a higher differential stress t than Mo 0.9 Wmore »