Computation and Data Driven Discovery of Topological Phononic Materials

© 2021, The Author(s). The discovery of topological quantum states marks a new chapter in both condensed matter physics and materials sciences. By analogy to spin electronic system, topological concepts have been extended into phonons, boosting the birth of topological phononics (TPs). Here, we present a high-throughput screening and data-driven approach to compute and evaluate TPs among over 10,000 real materials. We have discovered 5014 TP materials and grouped them into two main classes of Weyl and nodal-line (ring) TPs. We have clarified the physical mechanism for the occurrence of single Weyl, high degenerate Weyl, individual nodal-line (ring), nodal-link, nodal-chain, and nodal-net TPs in various materials and their mutual correlations. Among the phononic systems, we have predicted the hourglass nodal net TPs in TeO3, as well as the clean and single type-I Weyl TPs between the acoustic and optical branches in half-Heusler LiCaAs. In addition, we found that different types of TPs can coexist in many materials (such as ScZn). Their potential applications and experimental detections have been discussed. This work substantially increases the amount of TP materials, which enables an in-depth investigation of their structure-property relations and opens new avenues for future device design related to TPs

hannorein/rebound: 4.0.1

<ul> <li>Include missing python packages</li> </ul&gt

hannorein/rebound: 3.28.1

Improved support for reading old and corrupted SimulationArchives. Renamed ri_ias15.epsilon_global to ri_ias15.adaptive_mode. Added new timestep method for IAS15 ri_ias15.adaptive_mode = 2. This is experimental for now. Details to be described in Pham, Rein & Spiegel (in prep). Added unit tests to check for fused multiply add instruction (these break reproducibility). Added phony target in C Makefile to force rebuilding librebound whenever building examples

hannorein/rebound: 3.28.0

Native Windows support. REBOUND can now be built natively on Windows (without WSL) using the Microsoft Visual Studio Compiler. Python Wheels are now provided for Linux, MacOS, and Windows. This should significantly speed up the installation process on a wide variety of systems