文献类型：期刊文献

英文题名：Deep Learning-Assisted Fourier Analysis for High-Efficiency Structural Design: A Case Study on Three-Dimensional Photonic Crystals Enumeration

作者：Cui, Congcong[1];Wei, Guangfeng[1];Saba, Matthias[2,3];Cao, Yuanyuan[4];Han, Lu[1]

机构：[1]Tongji Univ, Sch Chem Sci & Engn, Shanghai, Peoples R China;[2]Univ Fribourg, Adolphe Merkle Inst, Fribourg, Switzerland;[3]Univ Fribourg, NCCR Bioinspired Mat, Fribourg, Switzerland;[4]East China Univ Sci & Technol, Sch Mat Sci & Engn, Shanghai, Peoples R China

年份：2026

卷号：22

期号：14

外文期刊名：SMALL

收录：;EI(收录号：20260319933529);WOS:【SCI-EXPANDED(收录号:WOS:001661069800001)】；

基金：This work was financially supported by the National Natural Science Foundation of China (Grant No. 22425303, 22472058) and the Fundamental Research Funds for the Central Universities.

语种：英文

外文关键词：complete photonic bandgap; deep learning; Fourier analysis; photonic crystal; structural design

摘要：The geometric design of structures with optimized physical and chemical properties is one of the core topics in materials science. However, designing new functional materials is challenging due to the vast number of existing and possible unknown structures to be enumerated and difficulties in mining the underlying correlations between structures and their properties. Here, we propose a universal method for periodic structural design and property optimization. The key in our approach is a deep-learning-assisted inverse Fourier transform, which enables the creation of arbitrary geometries within crystallographic space groups. It effectively explores extensive parameter spaces to identify ideal structures with desired properties. Taking the research of three-dimensional (3D) photonic structures as a case study, this method is capable of modelling numerous structures and identifying their photonic bandgaps in just a few hours. We confirmed the established knowledge that the widest photonic bandgaps exist in network morphologies, among which the single diamond (dia net) reigns supreme. Additionally, this method identified a rarely known lcs topology with excellent photonic properties, highlighting the infinitely extensible application boundaries of our approach. This work demonstrates the high efficiency and effectiveness of the Fourier-based method, advancing material design and providing insights for next-generation functional materials.