论文标题
基于机器学习模型的材料超导性能的预测
Prediction of superconducting properties of materials based on machine learning models
论文作者
论文摘要
超导材料的应用变得越来越普遍。传统上，新的超导材料的发现取决于专家的经验和大量的“反复试验”实验，这不仅增加了实验的成本，而且还延长了发现新的超导材料的时期。近年来，机器学习越来越多地应用于材料科学。基于此，该手稿建议使用XGBoost模型来识别超导体。深森林模型的首次应用来预测超导体的临界温度；深森林的首次应用来预测材料的带隙；并应用新的子网络模型来预测材料的费米能水平。与我们已知的类似文献相比，上述所有算法都达到了最新的。最后，该手稿使用上述模型搜索COD公共数据集并识别50个候选超导材料，其可能的临界温度可能大于90K。
The application of superconducting materials is becoming more and more widespread. Traditionally, the discovery of new superconducting materials relies on the experience of experts and a large number of "trial and error" experiments, which not only increases the cost of experiments but also prolongs the period of discovering new superconducting materials. In recent years, machine learning has been increasingly applied to materials science. Based on this, this manuscript proposes the use of XGBoost model to identify superconductors; the first application of deep forest model to predict the critical temperature of superconductors; the first application of deep forest to predict the band gap of materials; and application of a new sub-network model to predict the Fermi energy level of materials. Compared with our known similar literature, all the above algorithms reach state-of-the-art. Finally, this manuscript uses the above models to search the COD public dataset and identify 50 candidate superconducting materials with possible critical temperature greater than 90 K.
