Data-driven Advanced Computational Materials Modeling - Supplementail FIle

Developing scalable, interpretable AI tools integrating generative models to revolutionize computational materials discovery and enhance STEM education.

This project addresses challenges in computational materials modeling by creating scalable, interpretable AI tools. Current machine learning (ML) models face high computational costs, difficulty handling complex crystal symmetries, and a "black box" nature that limits interpretability. To address these issues, we aim to integrate nonnegative matrix factorization (NMF) with generative AI. NMF offers structure-aware capabilities, reducing computational costs and incorporating material symmetries, while generative AI produces high-fidelity synthetic datasets to augment limited data. Additionally, uncertainty quantification is included to enhance model reliability. This hybrid approach aims to develop efficient tools for high-throughput materials discovery and property prediction. The project outcomes include scalable algorithms, enhanced interpretability for clearer insights into materials, and advanced methodologies for materials discovery. Open-source tools, user-friendly interfaces, and educational materials will ensure broader accessibility, supporting AI literacy and fostering collaboration. This effort benefits both Lehigh's research community and broader STEM fields by advancing materials modeling and empowering diverse students through accessible AI tools and resources.