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Meeting 2027 TMS Annual Meeting & Exhibition
Symposium AI/ML/Data Informatics for Materials Discovery: Bridging Experiment, Theory, and Modeling
Presentation Title Accelerating Structural Materials Discovery by Orders of Magnitude Using Random Libraries
Author(s) Vivek Chawla, Dayakar Penumadu, Sergei Kalinin
On-Site Speaker (Planned) Vivek Chawla
Abstract Scope Rapid discovery of structural materials is critical for applications in extreme environments, including refractory alloys, nuclear systems, and hypersonic technologies, but is limited by slow synthesis and mechanical characterization. We present a high-throughput screening approach using random combinatorial libraries, where thousands of compositions are contained within a single specimen, identified by EDS, and mechanically characterized through automated nanoindentation. This strategy enables dense composition–property mapping while dramatically reducing the number of samples required to explore high-dimensional compositional spaces. An experimentally calibrated Monte Carlo framework evaluates practical constraints, including particle size, EDS resolution and noise, positioning accuracy, and nanoindenter motion overhead. The analysis identifies conditions under which random libraries outperform conventional synthesis-and-test workflows. We further demonstrate the complete experimental workflow through macro-to-micro EDS alignment, cluster-based site selection, and cost-aware Gaussian process planning for automated indentation, establishing random libraries as a scalable platform for accelerated structural materials discovery.
Proceedings Inclusion? Planned:
Keywords Machine Learning, Mechanical Properties, Characterization

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