PDF(Pubmed)
Abstract：
Superconductivity is a remarkable phenomenon in condensed matter physics, which comprises a fascinating array of properties expected to revolutionize energy-related technologies and pertinent fundamental research. However, the field faces the challenge of achieving superconductivity at room temperature. In recent years, Artificial Intelligence (AI) approaches have emerged as a promising tool for predicting such properties as transition temperature (Tc) to enable the rapid screening of large databases to discover new superconducting materials. This study employs the SuperCon dataset as the largest superconducting materials dataset. Then, we perform various data pre-processing steps to derive the clean DataG dataset, containing 13,022 compounds. In another stage of the study, we apply the novel CatBoost algorithm to predict the transition temperatures of novel superconducting materials. In addition, we developed a package called Jabir, which generates 322 atomic descriptors. We also designed an innovative hybrid method called the Soraya package to select the most critical features from the feature space. These yield R2 and RMSE values (0.952 and 6.45 K, respectively) superior to those previously reported in the literature. Finally, as a novel contribution to the field, a web application was designed for predicting and determining the Tc values of superconducting materials.
摘要：
超导是凝聚态物理中的一个显著现象,其中包括一系列令人着迷的特性，这些特性有望彻底改变与能源相关的技术和相关的基础研究。然而,该领域面临着在室温下实现超导性的挑战。近年来,人工智能（AI）方法已成为一种有前途的工具，用于预测诸如转变温度（Tc）之类的特性，从而能够快速筛选大型数据库以发现新的超导材料。本研究采用SuperCon数据集作为最大的超导材料数据集。然后，我们执行各种数据预处理步骤以导出干净的DataG数据集，含有13,022种化合物。在研究的另一个阶段，我们应用新的CatBoost算法来预测新型超导材料的转变温度。此外,我们开发了一个叫做Jabir的包，生成322个原子描述符。我们还设计了一种创新的混合方法，称为Soraya包，从功能空间中选择最关键的功能。这些产率R2和RMSE值(0.952和6.45K，分别)优于文献中先前报道的那些。最后,作为对该领域的新颖贡献，设计了一个Web应用程序来预测和确定超导材料的Tc值。
