Random Combinatorial Libraries and Automated Nanoindentation for High-Throughput Structural Materials Discovery

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 quantify practical limits, including particle size, EDS noise and resolution, positional accuracy, and nanoindenter motion costs. The simulations identify regimes where random libraries provide orders of magnitude acceleration over classical workflows. Finally, we demonstrate experimental navigation of these libraries using automated indentation. Together, these results establish random libraries as a general route to high throughput characterization in structurally critical material systems.