Collaborative Research: DMREF: Symmetry-Guided Machine Learning for the Discovery of Topological Phononic Materials

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Non-technical Description: 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. Technical Description: While the topology of electronic states has been a central theme in condensed matter physics for the past decade, topological phononic states have received much less attention. Unlike their fermionic counterparts, topological states in the entire phonon spectrum can contribute to observable material properties, making topological phononic materials ideal testbeds for emerging new physics in topological bosonic systems, including phonon thermal Hall effects, novel topological phonon-electron interactions and the resulting phenomena, such as unusual superconducting states. This project aims to systematically identify materials hosting intrinsic topological phonons in the thermal regime, where the topological phononic states explicitly modify intrinsic material properties, including thermal transport, electron-phonon interactions, and surface phonon modes. The research team will seek to accelerate material discovery by incorporating symmetry-guided machine learning based on Euclidean neural networks. Machine learning predictions will be verified using first-principles phonon simulation and topological invariance analysis. Promising candidate materials will be synthesized as thin films and bulk single crystals and characterized using inelastic neutron and x-ray scattering, thermal transport, and surface-sensitive spectroscopy and scanning probe measurements. This research will advance fundamental understanding of topological bosonic systems and examine new thermal functionalities enabled by topological phonons.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.