通过高级机器学习和创新特征工程预测超导转变温度
Predicting Superconducting Transition Temperature through Advanced Machine Learning and Innovative Feature Engineering
AI总结：
本研究基于SuperCon数据集构建DataG数据集，利用CatBoost算法、Jabir包生成原子描述符及Soraya包进行特征选择，实现了优于文献的超导转变温度预测，并开发了Web应用。
摘要：
超导是凝聚态物理学中的一个显著现象，包含一系列有望彻底改变能源相关技术和相关基础研究的迷人特性。然而，该领域面临着实现室温超导的挑战。近年来，人工智能（AI）方法已成为预测诸如转变温度（Tc）等特性的有前途的工具，从而能够对大型数据库进行快速筛选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，包含13022种化合物。在研究的另一阶段，我们应用新颖的CatBoost算法来预测新型超导材料的转变温度。此外，我们开发了一个名为Jabir的包，可生成322个原子描述符。我们还设计了一种名为Soraya包的创新混合方法，用于从特征空间中选择最关键的特征。这些产生了R2和RMSE值（分别为0.952和6.45 K），优于先前文献中报告的值。最后，作为对该领域的新贡献，设计了一个用于预测和确定超导材料Tc值的Web应用程序。
英文摘要：
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 13022 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 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.
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。
