论文标题
SCGAN：一种生成的对抗网络，可预测假设的超导体
ScGAN: A Generative Adversarial Network to Predict Hypothetical Superconductors
论文作者
论文摘要
尽管已经被发现了三十多年前，但高温超导体（HTSS）既缺乏对其机制的解释，也缺乏一种搜索它们的系统方式。为了帮助这一搜索，该项目提出了Scgan，Scgan是一种生成的对抗网络（GAN），以有效预测新的超导体。 SCGAN接受了OQMD的化合物的培训，然后将其传输到SuperCon数据库或其中的一个子集中。一旦受过训练，GAN被用来预测超导候选者，并且大约70％的人被分类模型确定为超导 - 与手动搜索方法相比，发现率增加了23倍。此外，超过99％的预测是新型材料，表明Scgan能够预测全新的超导体，包括几个有希望的HTS候选者。该项目提出了一种新的，有效的方法来寻找新的超导体，该导体可用于技术应用，也可以洞悉未解决的高温超导性问题。
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.
