The Materials Genome Initiative, a national effort to introduce new materials into the market faster and at lower cost, has made significant progress in computational simulation and modeling of materials. To build on this progress, a large amount of experimental data for validating these models, and informing more sophisticated ones, will be required. High-throughput experimentation generates large volumes of experimental data using combinatorial materials synthesis and rapid measurement techniques, making it an ideal experimental complement to bring the Materials Genome Initiative vision to fruition. This paper reviews the state-of-the-art results, opportunities, and challenges in high-throughput experimentation for materials design. A major conclusion is that an effort to deploy a federated network of high-throughput experimental (synthesis and characterization) tools, which are integrated with a modern materials data infrastructure, is needed.
NEXAFS
spectroscopy was used to investigate the temperature dependence
of thermally active ethylene-vinyl acetate | multiwall carbon nanotube
(EVA|MWCNT) films. The data shows systematic variations of intensities
with increasing temperature. Molecular orbital assignment of interplaying
intensities identified the 1s → π*C=C and 1s → π*C=O transitions as the
main actors during temperature variation. Furthermore, enhanced near-edge
interplay was observed in prestrained composites. Because macroscopic
observations confirmed enhanced thermal-mechanical actuation in prestrained
composites, our findings suggest that the interplay of C=C
and C=O π orbitals may be instrumental to actuation.
Recently there has been an ever-increasing trend in the use of machine learning (ML) and artificial intelligence (AI) methods by the materials science, condensed matter physics, and chemistry communities. This perspective article identifies key scientific, technical, and social opportunities that the materials community must prioritize to consistently develop and leverage Scientific AI (SciAI) to provide a credible path towards the advancement of current materials-limited technologies. Here we highlight the intersections of these opportunities with a series of proposed paths forward. The opportunities are roughly sorted from scientific/technical (e.g. development of robust, physically meaningful multiscale material representations) to social (e.g. promoting an AI-ready workforce). The proposed paths forward range from developing new infrastructure and capabilities to deploying them in industry and academia. We provide a brief introduction to AI in materials science and engineering, followed by detailed discussions of each of the opportunities and paths forward.
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