Advances and opportunities in high-throughput small-scale mechanical testing

作者：Daniel S. Gianola, Nicolò Maria della Ventura, Glenn H. Balbus, Patrick Ziemke, McLean P. Echlin, Matthew R. Begley · 发表于：Current Opinion in Solid State and Materials Science · 年份：2023 · DOI：10.1016/j.cossms.2023.101090 · 被引用次数：42 · 研究领域：Machine Learning in Materials Science、Electronic and Structural Properties of Oxides、Advanced X-ray and CT Imaging

The quest for novel materials used in technologies demanding extreme performance has been accelerated by advances in computational materials screening, additive manufacturing routes, and characterization probes. Despite tremendous progress, the pace of adoption of new materials has still not met the promise of global initiatives in materials discovery. This challenge is particularly acute for structural materials with thermomechanical and environmental demands whose performance depends on microstructure as well as material composition. In this prospective article, we review advances in high-throughput mechanical testing, and the associated specimen fabrication, materials characterization, and modeling tasks that show promise for acceleration of the materials development cycle. We identify a critical need to develop rapid testing and characterization strategies that faithfully reproduce design-relevant properties and circumvent the time and expense of conventional high fidelity testing. We identify small-scale mechanical testing workflows that can incorporate real-time decision making based on feedback from multimodal characterization and computational modeling. These workflows will require site-specific specimen fabrication procedures that are agnostic to the synthesis route and have the ability to modulate microstructure and defect characteristics. We close our review by conceptualizing a fully integrated high-throughput testing platform that addresses the speed-fidelity tra...