氢化物超导体临界转变温度的机器学习模型
收稿日期: 2024-05-08
修回日期: 2024-07-10
网络出版日期: 2024-07-29
基金资助
国家自然科学基金项目(52188101,52201030);国家重点研发计划项目(2021YFB3501503);中国科学院重点部署项目(ZDRW-CN-2021-2-5)
Machine Learning Model for Predicting the Critical Transition Temperature of Hydride Superconductors
Received date: 2024-05-08
Revised date: 2024-07-10
Online published: 2024-07-29
Supported by
National Natural Science Foundation of China(52188101,52201030);National Key Research and Development Program of China(2021YFB3501503);Key Research Program of Chinese Academy of Sciences(ZDRW-CN-2021-2-5)
高压下发现的具有高临界转变温度(Tc)的氢化物超导体激起了研究者对常压室温超导材料探索的广泛兴趣。尽管第一性原理方法可以准确预测氢化物超导体的Tc，但电声耦合计算量巨大且十分昂贵，因此迫切需要建立一个既准确又高效的Tc预测模型。本工作利用随机森林算法，根据特征的重要性选择最关键的特征，开发了一个简单且物理可解释的机器学习模型。该模型利用所选择的4个关键特征(即组成元素价电子数标准差、共价半径平均值和门捷列夫数(Mendeleev数)范围，以及Fermi能级处H的态密度占比)实现了高的Tc预测精度(平均绝对误差为24.3 K，均方根误差为33.6 K)，这为氢化物超导体的高通量筛选提供了有效预测模型，有助于加速高Tc超导氢化物的发现。
赵晋彬 , 王建韬 , 何东昌 , 李俊林 , 孙岩 , 陈星秋 , 刘培涛 . 氢化物超导体临界转变温度的机器学习模型[J]. 金属学报, 2024 , 60(10) : 1418 -1428 . DOI: 10.11900/0412.1961.2024.00140
The discovery of hydride superconductors with high critical transition temperature (Tc) under high pressures has received considerable interest in developing superconducting materials that can operate at room temperature and ambient pressure. Although first-principles methods can accurately predict the critical temperature of hydride superconductors, the computational demands are significant because of the expensive calculation of electron-phonon coupling. Hence, constructing an accurate and efficient model for predicting Tc is highly desirable. In this study, a simple and interpretable machine learning (ML) model was developed using the random forest algorithm, which enables the selection of important features based on their importance. Using four physics-based features, namely, the standard deviation of the number of valence electrons, mean covalent radii, range of the Mendeleev number of constituent elements, and hydrogen fraction of the total density of states at the Fermi energy, the optimal ML model achieves high accuracy, with a mean absolute error of 24.3 K and a root-mean-square error of 33.6 K. The ML model developed in this study shows great application potential for high-throughput screening, thereby expediting the discovery of high-Tc superconducting hydrides.
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