Symmetry-Guided Machine Learning for the Discovery of Topological Phononic Materials
Fundamental understanding and control of heat conduction processes in materials are important for energy infrastructure, electronic devices, and renewable energy generation systems. This project focuses on a novel property of phonons – vibrations of atoms that carry the heat in materials - called "topology". This property may allow new phenomena, such as heat conduction perpendicular to the temperature gradient direction and more efficient transport of heat waves on the material surfaces. To discover topological phonons, the research team will exploit a Materials Genome approach to search for materials hosting these special heat carriers. Once candidates are identified, the research team will synthesize and characterize them, and the results will be used to refine the search algorithm. The research team plans to establish a public database storing the heat conduction properties of a large number of materials.
This research will not only advance the fundamental understanding of how topology affects heat conduction in real materials, but also provide new routes to realizing unusual functionalities such as heat conductors that can be switched on and off. This project also supports educational activities to teach basic materials physics concepts to K-12 and undergraduate students through hands-on class projects and short courses. To promote diversity in the materials science workforce, the team also provides research opportunities to high school and undergraduate students from underrepresented minority communities.