Speaker
Description
Refractory complex concentrated alloys (RCCAs) represent a promising class of materials for next-generation high-temperature applications, where conventional Ni-based superalloys and ceramics reach their performance limits. However, their development is hindered by intrinsic brittleness at low temperatures and the vastness of their compositional design space, which exceeds billions of possible combinations.
To address these challenges, we propose a high-throughput experimental and data-driven approach to explore the interplay between chemical composition, microstructure, and ductility in model binary refractory systems. Thin-film materials libraries will be synthesized by combinatorial PVD, enabling systematic mapping of structure–property relationships across wide composition gradients. The rate sensitivity of deformation, which correlates with ductility, will be rapidly quantified using nanoindentation-based high-throughput mechanical screening.
The resulting datasets will serve as a foundation for training machine learning models capable of predicting mechanical properties not only within a single binary system but also across different ones. We aim to evaluate (i) how transferable such models are between systems with shared elements (e.g., from A–B to A–C or B–C), and (ii) whether integrating data from multiple binary systems enables reliable extrapolation to ternary or higher-order alloys.
Ultimately, this work seeks to establish a generalizable, scalable workflow that integrates combinatorial synthesis, rapid mechanical characterization, and machine learning to accelerate the discovery of ductile RCCAs for extreme-temperature environments.