Random combinatorial libraries and automated nanoindentation for high-throughput structural materials discovery

Accelerating the discovery of structural materials is essential for advancing applications in hard and refractory alloys, hypersonic platforms, nuclear systems, and other extreme-environment technologies. However, progress is often constrained by slow synthesis–characterization cycles and the need for extensive mechanical testing across large compositional spaces. Here, we propose a rapid screening strategy based on random materials libraries, where 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 exponentially reducing the number of samples needed to span high-dimensional composition spaces compared to traditional synthesis-and-test workflows. An experimentally calibrated Monte Carlo framework is developed to quantify practical limits, capturing minimal particle sizes for reliable measurement, EDS noise and resolution, positional accuracy, and nanoindenter motion and reconfiguration costs. The simulations identify regimes where random libraries can substantially accelerate exploration relative to classical workflows. Finally, we demonstrate the full experimental realization using macro-to-micro-EDS alignment, cluster-aware site selection, and automated indentation guided by cost-aware Gaussian-process planning. Together, these results demonstrate the potential of random libraries as a scalable route to high-throughput characterization in structurally critical material systems. Accelerating the discovery of structural materials is crucial for advancing technologies in extreme environments, yet traditional methods are hindered by slow synthesis and testing cycles. Here, the authors introduce a rapid screening strategy using random materials libraries, significantly reducing sample requirements and enabling efficient high-throughput characterization, demonstrating a scalable route to structurally critical materials exploration.