A deep learning approach to search for superconductors from electronic bands

Jun Li, Wenqi Fang, Shangjian Jin, Tengdong Zhang, Yanling Wu, Xiaodan Xu, Yong Liu, Dao-Xin Yao
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引用次数: 0

Abstract

Energy band theory is a foundational framework in condensed matter physics. In this work, we employ a deep learning method, BNAS, to find a direct correlation between electronic band structure and superconducting transition temperature. Our findings suggest that electronic band structures can act as primary indicators of superconductivity. To avoid overfitting, we utilize a relatively simple deep learning neural network model, which, despite its simplicity, demonstrates predictive capabilities for superconducting properties. By leveraging the attention mechanism within deep learning, we are able to identify specific regions of the electronic band structure most correlated with superconductivity. This novel approach provides new insights into the mechanisms driving superconductivity from an alternative perspective. Moreover, we predict several potential superconductors that may serve as candidates for future experimental synthesis.
从电子带搜索超导体的深度学习方法
能带理论是凝聚态物理学的基础框架。在这项工作中,我们采用了一种深度学习方法--BNAS,来寻找电子能带结构与超导转变温度之间的直接相关性。我们的研究结果表明,电子能带结构可以作为超导性的主要指标。为了避免过度拟合,我们利用了一个相对简单的深度学习神经网络模型,尽管该模型非常简单,但却展示了对超导特性的预测能力。通过利用深度学习中的注意力机制,我们能够识别出电子能带结构中与超导性最相关的特定区域。此外,我们还预测了几种潜在的超导体,它们可能成为未来实验合成的候选物质。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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