Theory-guided ML models achieve R²=0.98 for temperature-dependent yield strength in RCCAs and evaluate element impacts on phase stability and ductility, supporting an on-demand composition predictor and screener.
Pattern Recognition in Practice Iv: Multiple Paradigms, Comparative Studies and Hybrid Systems, 1994
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Composition design of refractory compositionally complex alloys using machine learning models
Theory-guided ML models achieve R²=0.98 for temperature-dependent yield strength in RCCAs and evaluate element impacts on phase stability and ductility, supporting an on-demand composition predictor and screener.