Random Combinatorial Libraries and Automated Nanoindentation for High-Throughput Structural Materials Discovery
Vivek Chawla, Dayakar Penumadu, Sergei Kalinin

TL;DR
This paper introduces a high-throughput method using random material libraries and automated nanoindentation to accelerate the discovery of structural materials by efficiently mapping composition-property relationships.
Contribution
It presents a novel approach combining random libraries, nanoindentation, and Monte Carlo simulations to significantly speed up materials discovery processes.
Findings
Random libraries enable dense composition-property mapping.
Monte Carlo simulations identify regimes with orders of magnitude acceleration.
Automated nanoindentation effectively navigates the libraries.
Abstract
Accelerating the discovery of structural materials is essential for applications in hard and refractory alloys, hypersonic platforms, nuclear systems, and other extreme environment technologies. Progress is often constrained by slow synthesis and characterization cycles and the need for extensive mechanical testing across large compositional spaces. Here, we propose a rapid screening strategy based on random material libraries, in which thousands of distinct compositions are embedded within a single specimen, mapped by EDS, and subsequently characterized. Using nanoindentation as a representative case, we show that such libraries enable dense composition property mapping while reducing the number of samples required to explore high dimensional composition spaces compared to traditional synthesis and test workflows. An experimentally calibrated Monte Carlo framework is developed to…
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Taxonomy
TopicsMachine Learning in Materials Science · Advanced Electron Microscopy Techniques and Applications · Advanced Materials Characterization Techniques
