Abstract
Topological materials present unconventional electronic properties that make them attractive for both basic science and next-generation technological applications. The majority of currently known topological materials have been discovered using methods that involve symmetry-based analysis of the quantum wave function. Here we use machine learning to develop a simple-to-use heuristic chemical rule that diagnoses with a high accuracy whether a material is topological using only its chemical formula. This heuristic rule is based on a notion that we term topogivity, a machine-learned numerical value for each element that loosely captures its tendency to form topological materials. We next implement a high-throughput procedure for discovering topological materials based on the heuristic topogivity-rule prediction followed by ab initio validation. This way, we discover new topological materials that are not diagnosable using symmetry indicators, including several that may be promising for experimental observation.
| Original language | English |
|---|---|
| Pages (from-to) | 772-778 |
| Number of pages | 7 |
| Journal | Nano Letters |
| Volume | 23 |
| Issue number | 3 |
| DOIs | |
| State | Published - Feb 8 2023 |
Funding
We thank Pawan Goyal for assisting preliminary work. We thank Samuel Kim, Peter Lu, Rumen Dangovski, and Edward Zhang for helpful discussions. We thank Feng Tang and Xiangang Wan for the sharing of materials data. We thank Paola Rebusco for critical reading and editing of the manuscript. A.M. acknowledges support from the MIT EECS Alan L. McWhorter Fellowship and from the National Science Foundation Graduate Research Fellowship under Grant No. 1745302. Research was sponsored in part by the United States Air Force Research Laboratory and the United States Air Force Artificial Intelligence Accelerator and was accomplished under Cooperative Agreement Number FA8750-19-2-1000. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the United States Air Force or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation herein. This material is also based upon work supported in part by the Air Force Office of Scientific Research under the awards number FA9550-20-1-0115, FA9550-21-1-0299, and FA9550-21-1-0317, as well as in part by the US Office of Naval Research (ONR) Multidisciplinary University Research Initiative (MURI) grant N00014-20-1-2325 on Robust Photonic Materials with High-Order Topological Protection. This material is also based upon work supported in part by the U.S. Army Research Office through the Institute for Soldier Nanotechnologies at MIT, under Collaborative Agreement Number W911NF-18-2-0048. This work is also supported in part by the National Science Foundation under Cooperative Agreement PHY-2019786 (The NSF AI Institute for Artificial Intelligence and Fundamental Interactions, http://iaifi.org/ ). The work of Y.Z. and L.F. was supported by DOE Office of Basic Energy Sciences, Division of Materials Sciences and Engineering under Award DE-SC0018945 (theoretical analysis) and DE-SC0019275 (band structure calculation). L.F. was partly supported by the Simons Investigator Award from Simons Foundation. The work of H.C.P. was partly supported by a Pappalardo Fellowship at MIT.
Keywords
- chemical intuition
- interpretability
- machine learning
- materials discovery
- topological materials