ScGAN：一种用于预测假设超导体的生成对抗网络
ScGAN: A Generative Adversarial Network to Predict Hypothetical Superconductors
AI总结：
提出ScGAN生成对抗网络，结合OQMD训练与SuperCon迁移学习，高效预测新型超导体，70%预测被验证为超导，发现率提升23倍，99%为新材料，为高温超导搜索提供新途径。
摘要：
尽管高温超导体（HTSs）被发现已有三十多年，但对其机理的解释和系统的搜索方法仍然缺乏。为助力这一搜索，本项目提出了ScGAN，一种用于高效预测新型超导体的生成对抗网络（GAN）。ScGAN在OQMD中的化合物上进行了训练，然后通过迁移学习应用于SuperCon数据库或其子集。训练完成后，该GAN被用于预测超导候选材料，其中约70%的候选材料被分类模型判定为具有超导性——与人工搜索方法相比，发现率提高了23倍。此外，超过99%的预测结果是新材料，表明ScGAN能够潜在地预测全新的超导体，包括若干有前景的高温超导候选材料。本项目提出了一种新颖、高效的搜索新型超导体的方法，该方法可能用于技术应用，或为高温超导这一未解问题提供见解。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
