A deep learning approach to search for superconductors from electronic bands

TL;DR

This paper introduces a deep learning method to analyze electronic band structures and predict superconductivity, providing new insights and potential superconductor candidates.

Contribution

The study applies a simple neural network with attention mechanisms to link electronic bands with superconductivity, offering a novel approach for predicting superconducting materials.

Findings

Electronic band structures can indicate superconductivity.

The neural network predicts potential superconductors.

Attention mechanisms highlight key band regions related to superconductivity.

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.