YANG Yikun;WU Zhen;LIU Honghao;ZHANG Zaoxiao;

• 1:School of Chemical Engineering and Technology
• 2:Xi'an Jiaotong University
• 3:State Key Laboratory of Green Hydrogen and Electricity

Abstract：

Metal hydride thermal/hydrogen energy storage material is considered ideal candidate due to high energy density,wide working temperature range and lack of corrosive pollutants. Multi-component metal hydride alloys can be formed by doping with different elements to obtain various target properties. However, conventional material development method relies on experimental synthesis,having the disadvantages of time-consuming and costly. Data-driven machine learning prediction model is capable of addressing this problem. By comparing varieties of regression algorithms such as least squares regression,least absolute shrinkage and selection operator regression,ridge regression,elastic net regression,supporting vector regression,and random forest regression,the relationship between the microscopic properties of metal hydride materials and their macroscopic formation energy are established. Results show that random forest regression have the best prediction performance,with lowest relative errors on both the training and test sets of 3.078 and 8.201 1,high R-squared values,and great generalization and regression abilities. SHAP analysis reveals extreme and mean value of ground state atom of metal hydride exhibit the greatest SHAP value of 5.56 and 1.26,suggesting their significant influence on the formation energy.Analysis for the prediction value of Mg-base,Ca-base,AB type,AB2 type,and AB5 type metal hydrides shows the highest relative error below 9%,further proving the accuracy and universality of the model for all types of metal hydride. This model can be used to predict the formation enthalpy of unknown datasets.

Key Words： thermal application of solar energy;metal hydride;hydrogen and heat storage;machine learning;performance comparison

Abstract:

Keywords:

Foundation: 国家自然科学基金面上资助项目（52376208;52176203）

Authors: YANG Yikun;WU Zhen;LIU Honghao;ZHANG Zaoxiao;

DOI: 10.13226/j.issn.1006-6772.HH24103101

References：

• [1]BELA?D F,AL-SARIHI A,AL-MESTNEER R. Balancing climate mitigation and energy security goals amid converging global energy crises:The role of green investments[J]. Renewable Energy,2023,205:534-542.
• [2]姚玉璧，郑绍忠，杨扬，等.中国太阳能资源评估及其利用效率研究进展与展望[J].太阳能学报，2022,43(10):524-535.YAO Yubi,ZHENG Shaozhong,YANG Yang,et al. Progress and prospects on solar energy resource evaluation and utilization efficiency in China[J]. Acta Energiae Solaris Sinica, 2022, 43(10):524-535.
• [3]BEZDUDNY A V,BLINOV D V,DUNIKOV D O. Single-stage metal hydride-based heat storage system[J]. Journal of Energy Storage,2023,68:107590.
• [4]MALLESWARARAO K,DUTTA P,MURTHY S S. Applications of metal hydride based thermal systems:A review[J]. Applied Thermal Engineering,2022,215:118816.
• [5]DANGWAL S,IKEDA Y,GRABOWSKI B,et al. Machine learning to explore high-entropy alloys with desired enthalpy for roomtemperature hydrogen storage:Prediction of density functional theory and experimental data[J]. Chemical Engineering Journal,2024,493:152606.
• [6]AGAFONOV A, PINEDA-ROMERO N, WITMAN M, et al.Destabilizing high-capacity high entropy hydrides via earth abundant substitutions:From predictions to experimental validation[J].Acta Materialia,2024,276:120086.
• [7]YIN Y, LI B, YUAN Z M, et al. Microstructure and improved hydrogen storage properties of Mg85Zn5Ni10 alloy catalyzed by Cr2O3 nanoparticles[J]. Journal of Physics and Chemistry of Solids,2019,134:295-306.
• [8]ZHONG H C,HUANG Y S,DU Z Y,et al. Enhanced Hydrogen Ab/De-sorption of Mg(Zn)solid solution alloy catalyzed by YH2/Y2O3 nanocomposite[J]. International Journal of Hydrogen Energy,2020,45(51):27404-27412.
• [9]RKHIS M,LAASRI S,TOUHTOUH S,et al. New insights into the electrochemical and thermodynamic properties of AB-type ZrNi hydrogen storage alloys by native defects and H-doping:Computational experiments[J]. International Journal of Hydrogen Energy,2023,48(27):10089-10097.
• [10]GU X D, WANG F, CHENG J L, et al. Positive correlation of Nb/Cr doping with dehydrogenation performance of ZrCo-based hydrides[J]. International Journal of Hydrogen Energy, 2023,48(67):26276-26287.
• [11]DONG S Y,WANG Y Y,LI J Y,et al. Exploration and design of Mg alloys for hydrogen storage with supervised machine learning[J]. International Journal of Hydrogen Energy, 2023,48(97):38412-38424.
• [12]MOOSAVI S M,JABLONKA K M,SMIT B. The role of machine learning in the understanding and design of materials[J]. Journal of the American Chemical Society,2020,142(48):20273-20287.
• [13]ZHAI S, XIE H P, CUI P, et al. A combined ionic Lewis acid descriptor and machine-learning approach to prediction of efficient oxygen reduction electrodes for ceramic fuel cells[J]. Nature Energy,2022,7:866-875.
• [14]MOOSAVI S M,NANDY A,JABLONKA K M,et al. Understanding the diversity of the metal-organic framework ecosystem[J].Nature Communications,2020,11(1):4068.
• [15]GHEYTANZADEH M, RAJABHASANI F, BAGHBAN A, et al.Estimating hydrogen absorption energy on different metal hydrides using Gaussian process regression approach[J]. Scientific Reports,2022,12(1):21902.
• [16]SAAL J E, KIRKLIN S, AYKOL M, et al. Materials design and discovery with high-throughput density functional theory:The open quantum materials database(OQMD)[J]. JOM, 2013,65(11):1501-1509.
• [17]KIRKLIN S, SAAL J E, MEREDIG B, et al. The open quantum materials database(OQMD):Assessing the accuracy of DFT formation energies[J]. NPJ Computational Materials, 2015, 1:15010.
• [18]JAIN A,ONG S P,HAUTIER G,et al. Commentary:The Materials Project:A materials genome approach to accelerating materials innovation[J]. APL Materials,2013,1(1):011002.
• [19]WARD L, AGRAWAL A, CHOUDHARY A, et al. A generalpurpose machine learning framework for predicting properties of inorganic materials[J]. NPJ Computational Materials, 2016, 2:16028.
• [20]RODRIGUEZ-GALIANO V,SANCHEZ-CASTILLO M,CHICAOLMO M,et al. Machine learning predictive models for mineral prospectivity:An evaluation of neural networks, random forest,regression trees and support vector machines[J]. Ore Geology Reviews,2015,71:804-818.
• [21]AKIBA T,SANO S,YANASE T,et al. Optuna:a next-generation hyperparameter optimization framework[C]//Proceedings of the25th ACM SIGKDD International Conference on Knowledge Discovery&Data Mining. Anchorage AK USA. ACM, 2019:2623–31.10. 1145/3292500.3330701.