Machine learning accelerated search for  new double perovskite oxide photocatalysis

Wan Xin-Yang; Zhang Ye-Hui; Lu Shuai-Hua; Wu Yi-Lei; Zhou Qiong-Hua; Wang Jin-Lan

doi:10.7498/aps.71.20220601

Abstract

Double perovskite oxide A₂BB'O₆ has better stability and wider bandgap range than ABO₃-type oxide, and exhibits great prospects in photocatalytic overall water splitting. However, owing to the diversity of crystal structure and constituents of perovskite oxide, rapidly and accurately searching for A₂BB'O₆ for photocatalyst is still a big challenge, both experimentally and theoretically. In this work, in order to screen out suitable double perovskite oxide photocatalysts, a multi-step framework combined with machine learning technique and first-principles calculations is proposed. Nearly 8000 candidates with proper bandgaps for water splitting are screened out from among more than 50000 A₂BB'O₆-type double perovskite oxides. Statistical analysis of the results shows that double perovskite oxides with d¹⁰ metal ions at B/B' sites are more likely to have good absorption of visible light, and the structural symmetry of double perovskite also has influence on the bandgap value. Furthermore, first-principles calculations demonstrate that Sr₂GaSbO₆, Sr₂InSbO₆ and K₂NbTaO₆ are non-toxic photocatalyst candidates with proper band edges for overall water splitting.

Keywords:

Authors and contacts

School of Physics, Southeast University, Nanjing 211189, China

Corresponding author: Zhou Qiong-Hua, qh.zhou@seu.edu.cn ; Wang Jin-Lan, jlwang@seu.edu.cn

Funds: Project supported by the National Key Research and Development Program of China (Grant Nos. 2017YFA0204800, 2021YFA1500700), the Natural Science Foundation of China (Grant Nos. 22033002, 22003009), and the Postgraduate Research & Practice Innovation Program of Jiangsu Province (Grant No. KYCX20_0075).

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References

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Cited By

图 1 基于两步机器学习算法的双钙钛矿氧化物筛选框架. 包括数据准备、特征选择、机器学习过程和DFT验证4个步骤

Figure 1. Multistep machine learning-based screening framework for double perovskite oxides. There are four steps including data collection, feature selection, machine learning process and DFT verification.

DownLoad: Full-Size Img PowerPoint

图 2 (a) 3种不同的钙钛矿构型; (b) 训练集中A位和B位元素出现的频率

Figure 2. (a) Three different perovskite structures; (b) occurrence frequency of A- and B-site elements in the training set.

DownLoad: Full-Size Img PowerPoint

图 3 (a) 分类模型中重要性前十的特征和混淆矩阵; (b)分类模型测试集ROC曲线和AUC值; (c)回归模型中重要性前十的特征; (d)回归模型测试集R², 均方误差, 平均绝对误差和解释方差

Figure 3. (a) Relative feature importance of top 10 most important features and confusion matrix for bandgap classification; (b) receiver operating characteristic (ROC) curve for bandgap classification test set, area under the ROC curve (AUC) is provided; (c) relative feature importance of top 10 most important features for bandgap regression; (d) performance of bandgap regression model, coefficient of determination (R²), mean square error (MSE), mean absolute error (MAE) and explained variance (EV) are provided.

DownLoad: Full-Size Img PowerPoint

图 4 (a) 预测的钙钛矿带隙值百分比统计图, 红色区域的代表双钙钛矿的 B/B' 位都是d⁰或d¹⁰金属离子, 灰色区域代表B/B' 位点中只有一个是 d⁰ 或 d¹⁰ 金属离子, 蓝色区域代表B/B' 位点不含d⁰ 或 d¹⁰ 金属离子; (b) B/B' 位点都是d⁰ 或d¹⁰金属离子的双钙钛矿带隙分布图, 绿色区域为可见光能量范围; (c)不同B/B' 位组分下双钙钛矿带隙统计图; (d)相同化学式下, 3种晶系结构的双钙钛矿带隙值, 其中B/B' 位都是d⁰或d¹⁰金属离子

Figure 4. (a) The percentage chart of predict set of bandgap values with the percentage of perovskites, red represents all B/B' sites are d⁰ or d¹⁰ metal ions, grey represents only one of B/B' sites is d⁰ or d¹⁰ metal ion, blue represents none of B/B' sites are d⁰ or d¹⁰ metal ion; (b) the perovskite bandgap distribution diagram, colored area represents visible light energy range; (c) pie chart of the distribution ratio of different B/B' site ions; (d) comparison of bandgap values of 3 different structures, B/B' sites are all with d⁰ or d¹⁰ metal ions.

DownLoad: Full-Size Img PowerPoint

图 5 (a) 29种菱方类结构双钙钛矿材料DFT带隙与机器学习预测带隙的比较; (b) 3种候选双钙钛矿相对于水的氧化还原势的HSE带边位置, 以及作为比较基准的SrTiO₃(立方相)带边位置

Figure 5. (a) DFT bandgap verification of 29 rhombohedral double perovskites in the prediction set; (b) the HSE band edge positions with respect to the water reduction and oxidation potential levels of selected double perovskites. SrTiO₃(cubic) is listed as a benchmark.

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表 A1 过渡金属计算时附加的Hubbard U值

Table A1. Hubbard U value for the transition metal elements.

元素 U 值

V 3.25
Mo 4.38
W 6.2
Ni 6.2
Mn 3.9
Fe 5.3
Cr 3.7
Co 3.32

DownLoad: CSV

表 A2 特征符号及含义

Table A2. The symbol of features and their corresponding meanings.

符号含义

N_a 原子数
R_a 原子半径
V_a 原子体积
R_cov 共价半径
E_a 电子亲合能
N_e 电子数
χ 鲍林电负性
H_f 形成热
N_m 门捷列夫数
N_p 周期
R_ion 离子半径
t 容忍因子

DownLoad: CSV

表 1 3种候选双钙钛矿材料的PBE带隙、HSE带隙及带隙类型

Table 1. PBE and HSE bandgap of three kinds of double perovskite candidates and their bandgap categories.

Formula E_g-PBE /eV E_g-HSE /eV Bandgap

Sr₂GaSbO₆ 1.37 2.80 indirect
Sr₂InSbO₆ 1.63 3.07 indirect
K₂NbTaO₆ 1.77 3.06 direct

DownLoad: CSV

[1]	Dorian J P, Franssen H T, Simbeck D R 2006 Energy Policy 34 1984 Google Scholar
[2]	Omer A M 2008 Renew. Sust. Energ. Rev. 12 2265 Google Scholar
[3]	Pfenninger S, Hawkes A, Keirstead J 2014 Renew. Sust. Energ. Rev. 33 74 Google Scholar
[4]	Liang K, Huang T, Yang K, Si Y, Wu H Y, Lian J C, Huang W Q, Hu W Y, Huang G F 2021 Phys. Rev. Appl. 16 054043 Google Scholar
[5]	Ameen S, Rub M A, Kosa S A, Alamry K A, Akhtar M S, Shin H S, Seo H K, Asiri A M, Nazeeruddin M K 2016 ChemSusChem 9 10 Google Scholar
[6]	Chen S, Takata T, Domen K 2017 Nat. Rev. Mater. 2 17050 Google Scholar
[7]	Hisatomi T, Kubota J, Domen K 2014 Chem. Soc. Rev. 43 7520 Google Scholar
[8]	Maeda K, Domen K 2010 J. Phys. Chem. Lett. 1 2655 Google Scholar
[9]	Kumar A, Kumar A, Krishnan V 2020 ACS Catal. 10 10253 Google Scholar
[10]	Peña M A, Fierro J L G 2001 Chem. Rev. 101 1981 Google Scholar
[11]	Ouyang Y, Li Y, Zhu P, Li Q, Gao Y, Tong J, Shi L, Zhou Q, Ling C, Chen Q, Deng Z, Tan H, Deng W, Wang J 2019 J. Mater. Chem. A 7 2275 Google Scholar
[12]	Grimaud A, May K J, Carlton C E, Lee Y L, Risch M, Hong W T, Zhou J, Shao-Horn Y 2013 Nat. Commun. 4 2439 Google Scholar
[13]	Yin W, Weng B, Ge J, Sun Q, Li Z, Yan Y 2019 Energy Environ. Sci. 12 442 Google Scholar
[14]	Sun H, Xu X, Song Y, Zhou W, Shao Z 2021 Adv. Funct. Mater. 31 2009779 Google Scholar
[15]	Aczel A A, Bugaris D E, Li L, Yan J, de la Cruz C, zur Loye H C, Nagler S E 2013 Phys. Rev. B 87 014435 Google Scholar
[16]	Zhou Q, Lu S, Wu Y, Wang J 2020 J. Phys. Chem. Lett. 11 3920 Google Scholar
[17]	Lu S, Zhou Q, Guo Y, Wang J 2022 Chem 8 769 Google Scholar
[18]	Lu S, Zhou Q, Guo Y, Zhang Y, Wu Y, Wang J 2020 Adv. Mater. 32 2002658 Google Scholar
[19]	Wu Y, Lu S, Ju M, Zhou Q, Wang J 2021 Nanoscale 13 12250 Google Scholar
[20]	Goldsmith B R, Esterhuizen J, Liu J, Bartel C J, Sutton C 2018 AlChE J. 64 2311 Google Scholar
[21]	Chen T, Guestrin C 2016 XGBoost: A Scalable Tree Boosting System (Association for Computing Machinery) pp785–794
[22]	Natekin A, Knoll A 2013 Front. Neurorob. 7
[23]	Hafner J 2008 J. Comput. Chem. 29 2044 Google Scholar
[24]	Perdew J P, Burke K, Ernzerhof M 1996 Phys. Rev. Lett. 77 3865 Google Scholar
[25]	Cai B, Chen X, Xie M, Zhang S, Liu X, Yang J, Zhou W, Guo S, Zeng H 2018 Mater. Horiz. 5 961 Google Scholar
[26]	Blöchl P E 1994 Phys. Rev. B 50 17953 Google Scholar
[27]	Monkhorst H J, Pack J D 1976 Phys. Rev. B 13 5188 Google Scholar
[28]	Aryasetiawan F, Karlsson K, Jepsen O, Schönberger U 2006 Phys. Rev. B 74 125106 Google Scholar
[29]	Becke A D 1993 J. Chem. Phys. 98 1372 Google Scholar
[30]	Curtarolo S, Setyawan W, Hart G L W, Jahnatek M, Chepulskii R V, Taylor R H, Wang S, Xue J, Yang K, Levy O, Mehl M J, Stokes H T, Demchenko D O, Morgan D 2012 Com. Mat. Sci. 58 218 Google Scholar
[31]	Saal J E, Kirklin S, Aykol M, Meredig B, Wolverton C 2013 JOM 65 1501 Google Scholar
[32]	Jain A, Ong S P, Hautier G, Chen W, Richards W D, Dacek S, Cholia S, Gunter D, Skinner D, Ceder G, Persson K A 2013 APL Mater. 1 011002 Google Scholar
[33]	Goldschmidt V M 1926 Naturwissenschaften 14 477 Google Scholar
[34]	Sun Q, Yin W 2017 J. Am. Chem. Soc. 139 14905 Google Scholar
[35]	Bartel C J, Sutton C, Goldsmith B R, Ouyang R, Musgrave C B, Ghiringhelli L M, Scheffler M 2019 Sci. Adv. 5 eaav0693 Google Scholar
[36]	Weng B, Song Z, Zhu R, Yan Q, Sun Q, Grice C G, Yan Y, Yin W 2020 Nat. Commun. 11 3513 Google Scholar
[37]	Filip-Marina R, Giustino F 2018 Proc. Natl. Acad. Sci. U. S. A. 115 5397 Google Scholar
[38]	Ye W, Chen C, Dwaraknath S, Jain A, Ong S P, Persson K A 2018 MRS Bull. 43 664 Google Scholar
[39]	Zhao X G, Yang J H, Fu Y, Yang D, Xu Q, Yu L, Wei S H, Zhang L 2017 J. Am. Chem. Soc. 139 2630 Google Scholar
[40]	Lu S, Zhou Q, Ma L, Guo Y, Wang J 2019 Small Methods 3 1900360 Google Scholar
[41]	Goodenough J B 2004 Rep. Prog. Phys. 67 1915 Google Scholar
[42]	Okada S, Ohzeki M, Taguchi S 2019 Sci. Rep. 9 13036 Google Scholar
[43]	Wahl R, Vogtenhuber D, Kresse G 2008 Phys. Rev. B 78 104116 Google Scholar
[44]	Liu P, Nisar J, Pathak B, Ahuja R 2012 Int. J. Hydrogen Energy 37 11611 Google Scholar
[45]	Chou H, Hwang B, Sun C 2013 New and Future Developments in Catalysis (Amsterdam: Elsevier) pp217–270
[46]	Inoue Y 2009 Energy Environ. Sci. 2 364 Google Scholar
[47]	Kudo A, Hijii S 1999 Chem. Lett. 28 1103 Google Scholar
[48]	Kudo A, Miseki Y 2009 Chem. Soc. Rev. 38 253 Google Scholar
[49]	Acar C, Dincer I, Naterer G F 2016 Int. J. Energy Res. 40 1449 Google Scholar
[50]	Kaspar T C, Sushko P V, Spurgeon S R, Bowden M E, Keavney D J, Comes R B, Saremi S, Martin L, Chambers S A 2019 Adv. Mater. Interfaces 6 1801428 Google Scholar
[51]	Greiner M T, Helander M G, Tang W, Wang Z B, Qiu J, Lu Z 2012 Nat. Mater. 11 76 Google Scholar
[52]	El-Sayed A, Borghetti P, Goiri E, Rogero C, Floreano L, Lovat G, Mowbray D J, Cabellos J L, Wakayama Y, Rubio A, Ortega J E, de Oteyza D G 2013 ACS Nano 7 6914 Google Scholar

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