Automated Crystal Structure Prediction and Property Optimization via Multi-Modal Data Fusion and Reinforcement Learning

**Abstract:** This research presents a novel framework for automated crystal structure prediction (CSP) and property optimization leveraging multi-modal data fusion and reinforcement learning (RL). We develop a system that combines information extracted from chemical composition (text), molecular graphs (structural data), and existing materials databases (feature vectors) to efficiently predict stable crystal structures and optimize properties such as band gap, hardness, and thermal conductivity. This approach significantly outperforms traditional CSP methods by integrating diverse data sources and utilizing RL to navigate the vast compositional and structural search space, ultimately accelerating the discovery of advanced materials.