Automated Resin Property Prediction and Optimization via HyperScore-Driven Multi-Modal Data Analysis

**Abstract:** This paper introduces a novel framework for predicting and optimizing resin properties using a data-driven approach incorporating multi-modal data ingestion, semantic decomposition, and a refined scoring system – the HyperScore. By combining unstructured data from technical documents, experimental data, and simulation results, this system leverages advanced natural language processing and graph neural networks to identify subtle correlations and predict complex material behavior. The HyperScore, a dynamically adjusted evaluation metric, prioritizes logical consistency, novelty of findings, impact forecasting, and reproducibility, guiding the optimization process towards desired properties with unprecedented accuracy and efficiency. The proposed framework is expected to accelerate the discovery and development of advanced resin formulations, significantly impacting industries ranging from aerospace to automotive and beyond, with an estimated market impact of $5 billion within five years.