Collaborative Research: Elements: Phonon Database Generation, Analysis, and Visualization for Data Driven Materials Discovery

Material databases and their related computing infrastructures have become the major cornerstone of current data driven and artificial intelligence based materials discovery. However, among the rich material properties of interest to the materials community, few databases have comprehensively included phonon properties, which are at the center of materials science and are key to discover materials with diverse functionalities. This project meets these urgent needs to generate a comprehensive phonon database along with analysis, visualization, navigation, and visualization tools, combined with multi-channel infrastructure-community communication and feedback. The phonon database will become an excellent complement to the currently widely used material databases. Developing such an infrastructure will be beneficial for all areas of materials science and engineering, accelerating the prediction, design, and synthesis of novel materials with various emerging applications in modern science and technology. The project will promote the engagement of underrepresented and minority students in research, equip engineering students with interdisciplinary expertise and frontier knowledge crucial to their future careers, and fulfill the mission to prepare a high-quality workforce for science, technology, and engineering. The project will also develop new course materials for undergraduate and graduate computational materials science courses. High-quality material property data has always been a major bottleneck for maximizing the potential of modern artificial intelligence and machine learning in large-scale computational material discovery, which has led to the discovery of numerous new inorganic crystalline materials that have dramatically improved the quality of human life. This project aims to deliver (1) a comprehensive phonon database of two major datasets: one consists of about 40,000 phonon dispersions and 15,000 lattice thermal conductivity of existing crystal structures that are thermodynamically stable and fully computed by first-principles; the other consists of phonon related properties of 300,000 structures predicted by deep learning models and partially validated by first-principles, (2) a web server for non-profit researchers to effectively retrieve, quickly navigate, visualize, and compare large pools of phonon band structures to pinpoint the materials of interest, and (3) toolkits for predicting phonon properties. Our multi-channel communicable new phonon database and user-interactive toolsets will benefit broad communities of material physics, chemistry, and engineering with new collaborative opportunities for novel materials discovery in many societally important areas. This award by the NSF Office of Advanced Cyberinfrastructure is jointly supported by the Division of Materials Research. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.