| Abstract Scope |
This study focuses on the computational design of Refractory Complex Concentrated Alloys (Re-RCCAs), leveraging ICME frameworks to integrate empirical data, machine learning models, and alloy design methodologies. Our approach optimizes Re-RCCA compositions for extreme environments requiring mechanical strength, thermophysical stability, and radiation tolerance.
We employ CALPHAD simulations, high-throughput semi-empirical calculations, and machine learning-based modeling to minimize waste and enhance efficiency. Machine learning predicts optimal compositions while reducing experimental iterations and material waste, following a Lean Six Sigma approach.
The ICME workflow involves literature review, element selection, HEAPS screening, and CALPHAD modeling to predict phase stability. Machine learning then co-optimizes alloy compositions, significantly reducing trials. This efficient, cost-effective process maximizes resource use and minimizes loss of expensive powders, highlighting ICME's potential in advanced alloy design. |