面向复合材料带隙预测的两段式集成学习模型构建

doi:10.12066/j.issn.1007-2861.2389

Abstract

Abstract:

The band gap is an important parameter that can affect the physical and chemical properties of perovskite oxide composites, such as their conductivity and photo-electricity. To identify new perovskites for different applications, their band gap should be predicted via machine learning. Herein, a two-stage ensemble learning model that can predict the band gap of perovskite oxide composites is proposed by combining multiple individual base learners via a certain strategy. The first stage involves individual test functions produced by multiple regression learners. All individual base learners and some specific descriptors are aggregated into an ensemble model in the second stage. Subsequently, a dataset comprising the data of 210 ABX$_3$-type perovskites is used to evaluate the proposed ensemble learning model. Results show that the proposed two-stage ensemble methodology can improve the generalization performance. Its successful application on ABX$_3$-type perovskites indicates the effectiveness and practicability of ensemble learning in material research.

Key words: ensemble learning model, combination strategy, band gap predicting, perovskite oxides composite, generalization performance

CLC Number:

TB 331

XU Yan, HU Hongqing, LIU Xi, ZHANG Yufeng, DING Guangtai, ZHANG Huiran. Two-stage ensemble learning model for predicting band gaps of composites[J]. Journal of Shanghai University（Natural Science Edition）, 2022, 28(3): 504-511.

Figures/Tables 6

Fig. 1

Fig.2

Fig.3

Table 1

Fig.4

Fig.5

References 20

[1]	李梓进, 王维燕, 李红江, 等. 钙钛矿/晶硅叠层太阳电池关键材料与技术研究进展[J]. 材料导报, 2020, 34(11): 21061-21071.
[2]	杨潇, 曹成龙, 胡书, 等. 混合卤化物钙钛矿中的光致相分离效应[J]. 材料导报, 2022, 16: 1-27.
[3]	Karmakar A, Bernard G, Meldrum A, et al. Tailorable indirect to direct band-gap double perovskites with bright white-light emission: decoding chemical structure using solid-state NMR[J]. J Am Chem Soc, 2020, 142: 10780-10793. doi: 10.1021/jacs.0c02198 pmid: 32426971
[4]	Lu S, Zhou Q, Ouyang Y, et al. Accelerated discovery of stable lead-free hybrid organic-inorganic perovskites via machine learning[J]. Nat Commun, 2018, 9: 1-8. doi: 10.1038/s41467-017-02088-w
[5]	Gu T, Lu W, Bao X, et al. Using support vector regression for the prediction of the band gap and melting point of binary and ternary compound semiconductors[J]. Solid State Sci, 2006, 8(2): 129-136. doi: 10.1016/j.solidstatesciences.2005.10.011
[6]	Jain D, Chaube S, Khullar P, et al. Bulk and surface DFT investigations of inorganic halide perovskites screened using machine learning and materials property databases[J]. Phys Chem Chem Phys, 2019, 21: 19423-19436. doi: 10.1039/C9CP03240A
[7]	Liu H, Cheng J, Dong H, et al. Screening stable and metastable ABO$_3$ perovskites using machine learning and the materials project[J]. Comput Mater Sci, 2020, 177: 109614. doi: 10.1016/j.commatsci.2020.109614
[8]	Pilania G, Mannodi-Kanakkithodi A, Uberuaga B, et al. Machine learning bandgaps of double perovskites[J]. Sci Rep, 2016, 6: 1-10. doi: 10.1038/s41598-016-0001-8
[9]	Dey P, Bible J, Datta S, et al. Informatics-aided bandgap engineering for solar materials[J]. Comput Mater Sci, 2014, 83: 185-95. doi: 10.1016/j.commatsci.2013.10.016
[10]	Lee J, Seko A, Shitara K, et al. Prediction model of band gap for inorganic compounds by combination of density functional theory calculations and machine learning techniques[J]. Phys Rev B, 2016, 93: 1-12. doi: 10.1103/PhysRev.93.1
[11]	周钢, 郭福亮. 集成学习方法研究[J]. 计算技术与自动化, 2018, 37(4): 148-153.
[12]	徐永林, 王香蒙, 李鑫, 等. 基于高通量计算及机器学习的新材料带隙预测[J]. 中国科学: 技术科学, 2019, 49(1): 44-54.
[13]	Andrii T, Zoia D, Ivan I, et al. Sm-co alloys coercivity prediction using stacking heteroge- neous ensemble model[J]. Acta Metallurgica Slovaca, 2021, 27(4): 195-202. doi: 10.36547/ams.27.4.1173
[14]	Wang Z, Han Y, Lin X, et al. An Ensemble Learning Platform for the Large-Scale Exploration of New Double Perovskites[J]. ACS Appl Mater Interfaces, 2022, 14: 717-725. doi: 10.1021/acsami.1c18477
[15]	Liu Y, Wu J, Wang Z, et al. Predicting creep rupture life of Ni-based single crystal superalloys using divide-and-conquer approach based machine learning[J]. Acta Materialia, 2020, 195: 454467.
[16]	Zhang C, Ma Y. Ensemble machine learning: methods and applications[M]. Berlin: Springer, 2012.
[17]	Zhang Z, Tehrani A, Oliynyk A, et al. Finding the next superhard material through ensemble learning[J]. Advanced Materials, 2021, 33: 2020005112.
[18]	Stukowski A. Structure identification methods for atomistic simulations of crystalline materials[J]. Model Simul Mater Sci Eng, 2012, 20: 045021. doi: 10.1088/0965-0393/20/4/045021
[19]	Brendel A. Some comments on the evaluation of transducer performance[J]. Exp Tech, 1983, 7: 20-21.
[20]	闫岭岭, 江峰, 杜军威, 等. 基于混合采样与 Random_Stacking 的软件缺陷预测[J]. 计算机与现代化, 2021, 8: 70-76.

Two-stage ensemble learning model for predicting band gaps of composites

RichHTML

PDF

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 6

References 20

Related Articles 3

Recommended Articles

Metrics

Comments

[1]	HU Rui, LIU Qing, ZHANG Guangjie, LI Junjie, CHEN Xiaoyu, WEI Xiao, DAI Dongbo. Phase stability prediction of hign entropy alloys in aluminum matrix composites based on feature engneering and machine learning [J]. Journal of Shanghai University（Natural Science Edition）, 2022, 28(3): 476-484.
[2]	ZHANG Bing-feng, YANG Fang, ZHOU Ji-ming, QI Le-hua. Preparation of Alumina Short Fiber Preform Using Mold Pressing Technique in Vacuum and Its Quality [J]. Journal of Shanghai University（Natural Science Edition）, 2014, 20(1): 59-67.
[3]	ZHOU Ji-ming, ZHENG Wu-qiang, QI Le-hua, MA Yu-qin. Investigation on Compressive Failure Mechanism of 2D Cross-ply C_f/Al Composites by Extrusion Directly Following Vacuum Pressure Infiltration Process [J]. Journal of Shanghai University（Natural Science Edition）, 2014, 20(1): 75-82.