A Machine Learning Approach for Understanding and Discovering Topological Materials

Topological materials are of significant interest for both basic science and next-generation technological applications due to their unconventional electronic properties. The majority of currently-known topological materials have been discovered using methods that involve symmetry-based analysis of the quantum mechanical wavefunction. Here we use machine learning to develop a heuristic chemical rule, which diagnoses whether a material is topological using only its chemical formula. It is based on a notion that we term topogivity, which is a learned numerical value for each element that loosely captures the tendency of an element to form topological materials. Topogivities provide chemical insights for understanding topological materials. We implement a high-throughput procedure for discovering topological materials that are not diagnosable by symmetry indicators. The procedure is based on heuristic rule prediction followed by ab initio validation. The concept of topogivity represents a fundamentally new approach to the study of topological materials, and opens up new directions of research at the intersection of chemistry, machine learning, and band topology.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2021

Author dc:creator

• Ma, Andrew

Advisor dc:contributor.advisor

• Soljačić, Marin

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright MIT

Licence dc:rights.uri

• http://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/143926

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/143926