Machine Learning Strategies for Establishing Process-Structure-Property Linkages in Complex Concentrated Alloys

This thesis aims to give a detailed overview of the exploration of the extensive composition space presented by complex concentrated alloys (CCA) and their properties using machine learning (ML) methods. Interpretable models with the ability to trace the evolution across processes generate stable process–structure–property linkages (PSP) to enshrine the learnings of the ML models. A novel framework is presented that can extract the effect of thermo–mechanical processing on the crystal structure–mechanical property linkages of CCAs. ML–based PSP linkages could help capture higher order dependencies between mechanical properties, phase evolution, composition and processing information.