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arXiv 2211.03075cond-mat.supr-concs.LG
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基于机器学习模型的材料超导性能预测

Prediction of superconducting properties of materials based on machine learning models

Southwest Jiaotong University(西南交通大学)

Jie Hu, Yongquan Jiang, Yang Yan, Houchen Zuo

摘要:

超导材料的应用越来越广泛。传统上,新超导材料的发现依赖于专家的经验和大量的“试错”实验,这不仅增加了实验成本,也延长了发现新超导材料的周期。近年来,机器学习越来越多地应用于材料科学。基于此,本文提出了利用XGBoost模型识别超导体;首次应用深林模型预测超导体的临界温度;首次应用深林模型预测材料的带隙;应用新的子网络模型预测材料的费米能级。最后,本文利用上述模型对COD公共数据集进行了搜索,识别出了50种可能的临界温度大于90 K的候选超导材料。

英文摘要:

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.

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。

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