Automated Validation of Metallic Alloying Kinetics via Multi-Modal Data Fusion and HyperScore Prediction

**Abstract:** This research proposes a novel framework for accelerated and automated validation of metallic alloying kinetics—specifically, the solid-state diffusion behavior of aluminum within nickel-based superalloys—using multi-modal data fusion and a HyperScore prediction model. Traditional alloy development relies on time-consuming and resource-intensive diffusion couple experiments, often hampered by subjective interpretation of microscopy data. Our system ingests data from multiple sources including electron backscatter diffraction (EBSD), energy-dispersive X-ray spectroscopy (EDS), and thermodynamic modeling outputs, parses this information, and utilizes a multi-layered evaluation pipeline to generate a HyperScore indicative of process validity. This system promises a 10x acceleration in material screening and optimization while significantly reducing experimental costs, paving the way for rapid development of high-performance superalloys for next-generation aerospace applications.