Kavli Affiliate: Jing Wang
| First 5 Authors: Haosheng Xu, Yadong Jiang, Huan Wang, Jing Wang,
| Summary:
Topological materials with unconventional electronic properties have been
investigated intensively for both fundamental and practical interests.
Thousands of topological materials have been identified by symmetry-based
analysis and ab initio calculations. However, the predicted magnetic
topological insulators with genuine full band gaps are rare. Here we employ
this database and supervisedly train neural networks to develop a heuristic
chemical rule for electronic topology diagnosis. The learned rule is
interpretable and diagnoses with a high accuracy whether a material is
topological using only its chemical formula and Hubbard $U$ parameter. We next
evaluate the model performance in several different regimes of materials.
Finally, we integrate machine-learned rule with ab initio calculations to
high-throughput screen for magnetic topological insulators in 2D material
database. We discover 6 new classes (15 materials) of Chern insulators, among
which 4 classes (7 materials) have full band gaps and may motivate for
experimental observation. We anticipate the machine-learned rule here can be
used as a guiding principle for inverse design and discovery of new topological
materials.
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