Deep learning approach to genome of two-dimensional materials with flat electronic bands
Deep learning approach to genome of two-dimensional materials with flat electronic bands
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DOI：
10.1038/s41524-023-01056-x
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发表时间：
2022-07
影响因子：
9.7
通讯作者：
A. Bhattacharya;I. Timokhin;R. Chatterjee;Qian Yang;A. Mishchenko
中科院分区：
文献类型：
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作者：
A. Bhattacharya;I. Timokhin;R. Chatterjee;Qian Yang;A. Mishchenko
电子-电子关联在凝聚态物理中发挥着核心作用，支配着从超导到磁性的各种现象，以及许多技术应用。具有平坦电子能带的二维(2D)材料因其高度定域化的电子而为探索相互作用驱动的物理提供了天然的游乐场。对2D平带材料的搜索已经吸引了大量的努力，特别是现在，开放的科学数据库包含了数千种具有计算电子能带的材料。在这里，我们通过结合有监督和无监督的机器学习算法，使原本令人望而生畏的材料搜索和分类任务自动化。为此，使用卷积神经网络来识别2D平带材料，然后使用双层无监督学习算法进行基于对称性的分析。这种探索材料数据库的混合方法使我们能够构建包含平带的2D材料基因组，并揭示已知平带范例之外的材料类。
Electron-electron correlations play central role in condensed matter physics, governing phenomena from superconductivity to magnetism and numerous technological applications. Two-dimensional (2D) materials with flat electronic bands provide natural playground to explore interaction-driven physics, thanks to their highly localized electrons. The search for 2D flat band materials has attracted intensive efforts, especially now with open science databases encompassing thousands of materials with computed electronic bands. Here we automate the otherwise daunting task of materials search and classification by combining supervised and unsupervised machine learning algorithms. To this end, convolutional neural network was employed to identify 2D flat band materials, which were then subjected to symmetry-based analysis using a bilayer unsupervised learning algorithm. Such hybrid approach of exploring materials databases allowed us to construct a genome of 2D materials hosting flat bands and to reveal material classes outside the known flat band paradigms.