Machine learning for materials discovery: two-dimensional topological insulators

TL;DR

This paper demonstrates machine learning models trained on ab initio data to efficiently identify two-dimensional topological insulators, discovering 56 candidates including 17 novel insulators, significantly accelerating materials discovery.

Contribution

It introduces a machine learning framework that predicts the topological nature of 2D materials with over 90% accuracy, enabling rapid screening of large materials databases.

Findings

Identified 56 non-trivial 2D topological materials.

Discovered 17 novel insulating candidates.

Achieved 10× more efficient screening than trial-and-error.

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 novel insulating candidates for further investigation, for which we corroborate their topological properties with density functional theory calculations. This strategy is 10$\times$ more efficient than the trial-and-error approach while few orders of magnitude faster and is a proof of concept for guiding improved materials discovery search strategies.