Research on Flat-Band Feature Recognition Model Based on Energy Band Images

Junlong Li

doi:10.47297/taposatWSP2633-456922.20260705

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Research on Flat-Band Feature Recognition Model Based on Energy Band Images

7卷第5期

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Research on Flat-Band Feature Recognition Model Based on Energy Band Images
Research on Flat-Band Feature Recognition Model Based on Energy Band Images
College of Science, China Jiliang University, Hangzhou, Zhejiang, 310018, China
2026年7卷第5期页码：115-119
网络首发：2026-07-15，

纸质出版：2026-05-30
DOI：10.47297/taposatWSP2633-456922.20260705
Accepted：

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Junlong Li. (2026). Research on Flat-Band Feature Recognition Model Based on Energy Band Images. 科技理论与实践（英文版）, 7(5), 115-119.

Li Junlong.Research on Flat-Band Feature Recognition Model Based on Energy Band Images[J].Theory and Practice of Science and Technology,2026,07(05):115-119.
Junlong Li. (2026). Research on Flat-Band Feature Recognition Model Based on Energy Band Images. 科技理论与实践（英文版）, 7(5), 115-119. DOI： 10.47297/taposatWSP2633-456922.20260705.

Li Junlong.Research on Flat-Band Feature Recognition Model Based on Energy Band Images[J].Theory and Practice of Science and Technology,2026,07(05):115-119. DOI： 10.47297/taposatWSP2633-456922.20260705.

摘要

Abstract

Topological materials

including topological insulators

superconductors

and semimetals

have attracted extensive attention in condensed matter physics and materials science. In particular

flat-band topological semimetals are closely related to strongly correlated electron behaviors and unconventional superconductivity due to their extremely low bandwidth and high density of states. To improve the recognition of local low-dispersion bands

this work proposes a convolutional neural network framework

MEA-CNN

which integrates attention mechanisms and a mixture of experts mechanism for flat band recognition in band structure images. The model enhances key flat-band regions through channel and spatial attention

while the MoE structure adaptively models different flat band patterns. Experimental results show that MEA-CNN achieves an accuracy of 0.928 and an AUC of 0.9842

outperforming conventional convolutional neural networks with good interpretability.

关键词

Keywords

references

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