Machine learning for materials discovery: Two-dimensional topological insulators

Author(s)

Schleder, Gabriel R. ^{[1, 2, 3]} ; Focassio, Bruno ^{[1, 3]} ; Fazzio, Adalberto ^{[1, 3]}

Affiliation

^[1] Fed Univ ABC UFABC, BR-09210580 Santo Andre, SP - Brazil

^[2] Harvard Univ, John A Paulson Sch Engn & Appl Sci, Cambridge, MA 02138 - USA

^[3] Brazilian Nanotechnol Natl Lab LNNano, CNPEM, BR-13083970 Campinas, SP - Brazil

• Document type: Journal article
• Source: APPLIED PHYSICS REVIEWS; v. 8, n. 3 SEP 2021.
• Web of Science Citations: 0

Abstract

One of the main goals and challenges of materials discovery is to find the best candidates for each interest property or application. Machine learning rises in this context to efficiently optimize this search, exploring the immense materials space, consisting of simultaneously the atomic, compositional, and structural spaces. Topological insulators, presenting symmetry-protected metallic edge states, are a promising class of materials for different applications. However, further development is limited by the scarcity of viable candidates. Here we present and discuss machine learning-accelerated strategies for searching the materials space for two-dimensional topological materials. We show the importance of detailed investigations of each machine learning component, leading to different results. Using recently created databases containing thousands of ab initio calculations of 2D materials, we train machine learning models capable of determining the electronic topology of materials, with an accuracy of over 90%. We can then generate and screen thousands of novel materials, efficiently predicting their topological character without the need for a priori structural knowledge. We discover 56 non-trivial materials, of which 17 are novel insulating candidates for further investigation, for which we corroborate their topological properties with density functional theory calculations. This strategy is 10x more efficient than the trial-and-error approach while a few orders of magnitude faster and is a proof of concept for guiding improved materials discovery search strategies. (AU)

• FAPESP's process: 17/02317-2 - Interfaces in materials: electronic, magnetic, structural and transport properties
• Grantee: Adalberto Fazzio
• Support Opportunities: Research Projects - Thematic Grants
• FAPESP's process: 17/18139-6 - Machine learning for Materials Science: 2D materials discovery and design
• Grantee: Gabriel Ravanhani Schleder
• Support Opportunities: Scholarships in Brazil - Doctorate
• FAPESP's process: 19/04527-0 - Interface between crystalline topological insulators and 2D-trivial materials: defect proximity study
• Grantee: Bruno Focassio
• Support Opportunities: Scholarships in Brazil - Doctorate (Direct)