Machine Learning Model for Predicting the Critical Transition Temperature of Hydride Superconductors

doi:10.11900/0412.1961.2024.00140

Acta Metall Sin

2024, Vol. 60

Issue (10): 1418-1428 DOI: 10.11900/0412.1961.2024.00140

Research paper

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Machine Learning Model for Predicting the Critical Transition Temperature of Hydride Superconductors

ZHAO Jinbin¹^,², WANG Jiantao²^,³, HE Dongchang²^,³, LI Junlin¹, SUN Yan², CHEN Xing-Qiu²(

), LIU Peitao²(

)

1 School of Materials Science and Engineering, Taiyuan University of Science and Technology, Taiyuan 030024, China
2 Shenyang National Laboratory for Materials Science, Institute of Metal Research, Chinese Academy of Sciences, Shenyang 110016, China
3 School of Materials Science and Engineering, University of Science and Technology of China, Shenyang 110016, China

Cite this article:

ZHAO Jinbin, WANG Jiantao, HE Dongchang, LI Junlin, SUN Yan, CHEN Xing-Qiu, LIU Peitao. Machine Learning Model for Predicting the Critical Transition Temperature of Hydride Superconductors. Acta Metall Sin, 2024, 60(10): 1418-1428.

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Abstract

The discovery of hydride superconductors with high critical transition temperature (T_c) 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 T_c 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-T_c superconducting hydrides.

Key words: hydride superconductor superconducting transition temperature machine learning random forest first-principles calculation

Received: 08 May 2024

ZTFLH:

TG132.26

Fund: 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)

Corresponding Authors: LIU Peitao, professor, Tel: (024)23971560, E-mail: ptliu@imr.ac.cn;
CHEN Xing-Qiu, professor, Tel: (024)23971560, E-mail: xingqiu.chen@imr.ac.cn

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	ZHAO Jinbin
	WANG Jiantao
	HE Dongchang
	LI Junlin
	SUN Yan
	CHEN Xing-Qiu
	LIU Peitao

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https://www.ams.org.cn/EN/10.11900/0412.1961.2024.00140 OR https://www.ams.org.cn/EN/Y2024/V60/I10/1418

Fig.1 Distribution of hydride superconductors
(a) critical temperature (T_c) vs pressure
(b) number of binary and ternary hydrides in different T_c distribution intervals

Table 1 Hyper-parameters used in the sklearn library for the random forest algorithm (For other parameters that are not explicitly specified, default values are used)

Fig.2 Model test errors as a function of number of features for one single training (a) and averaged model test errors of ten independent trainings as a function of training set ratio (b) (The shadow-filled areas show the standard deviation. MAE—mean absolute error, RMSE—root mean square error)

Fig.3 The most important 4 features by recursive selections (a) and T_c predicted by the most accurate machine learning model obtained vs the ground-truth values (b) ([Avg_dev(NValence)]—standard deviation of the number of valence electrons of constituent element, [Mean(CovalentRadius)]—mean covalent radius of constituent element, [Range(Mendeleev Number)]—range of the Mendeleev number of constituent element, HDOS Fraction—hydrogen fraction of the total density of states at the Fermi energy)

Fig.4 Correlation between T_c and four features of Avg_dev(NValence) (a), Mean(CovalentRadius) (b), Range(Mendeleev Number) (c), and HDOS Fraction (d)

Table 2 Model predictions for the six hydride superconductors with the highest T_c in the dataset^{[43,59,60,70,89]}

Fig.5 Crystal structures and electronic densities of states for the six hydride superconductors with the highest T_c in the dataset
(a) Li₂MgH₁₆ (250 GPa) (b) CaHfH₁₂ (190 GPa) (c) CaHfH₁₈ (300 GPa)
(d) CaZrH₁₂ (300 GPa) (e) MgH₁₂ (500 GPa) (f) YH₁₀ (250 GPa)

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