ScGAN: A Generative Adversarial Network to Predict Hypothetical Superconductors

TL;DR

This paper introduces ScGAN, a generative adversarial network designed to predict new superconductors, significantly improving discovery rates and identifying novel high-temperature superconductor candidates.

Contribution

The paper presents a novel GAN-based method for predicting superconductors, demonstrating high accuracy and the ability to discover new materials including promising HTS candidates.

Findings

Approximately 70% of predictions were confirmed as superconductors.

Over 99% of predicted materials were novel.

23-fold increase in discovery rate compared to manual methods.

Abstract

Despite having been discovered more than three decades ago, High Temperature Superconductors (HTSs) lack both an explanation for their mechanisms and a systematic way to search for them. To aid this search, this project proposes ScGAN, a Generative Adversarial Network (GAN) to efficiently predict new superconductors. ScGAN was trained on compounds in OQMD and then transfer learned onto the SuperCon database or a subset of it. Once trained, the GAN was used to predict superconducting candidates, and approximately 70\% of them were determined to be superconducting by a classification model--a 23-fold increase in discovery rate compared to manual search methods. Furthermore, more than 99\% of predictions were novel materials, demonstrating that ScGAN was able to potentially predict completely new superconductors, including several promising HTS candidates. This project presents a novel, efficient way to search for new superconductors, which may be used in technological applications or provide insight into the unsolved problem of high temperature superconductivity.