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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

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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
cstr: 32037.14.aps.71.20220601
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  • 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.
      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).
    [1]

    Dorian J P, Franssen H T, Simbeck D R 2006 Energy Policy 34 1984Google Scholar

    [2]

    Omer A M 2008 Renew. Sust. Energ. Rev. 12 2265Google Scholar

    [3]

    Pfenninger S, Hawkes A, Keirstead J 2014 Renew. Sust. Energ. Rev. 33 74Google 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 054043Google 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 10Google Scholar

    [6]

    Chen S, Takata T, Domen K 2017 Nat. Rev. Mater. 2 17050Google Scholar

    [7]

    Hisatomi T, Kubota J, Domen K 2014 Chem. Soc. Rev. 43 7520Google Scholar

    [8]

    Maeda K, Domen K 2010 J. Phys. Chem. Lett. 1 2655Google Scholar

    [9]

    Kumar A, Kumar A, Krishnan V 2020 ACS Catal. 10 10253Google Scholar

    [10]

    Peña M A, Fierro J L G 2001 Chem. Rev. 101 1981Google 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 2275Google 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 2439Google Scholar

    [13]

    Yin W, Weng B, Ge J, Sun Q, Li Z, Yan Y 2019 Energy Environ. Sci. 12 442Google Scholar

    [14]

    Sun H, Xu X, Song Y, Zhou W, Shao Z 2021 Adv. Funct. Mater. 31 2009779Google 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 014435Google Scholar

    [16]

    Zhou Q, Lu S, Wu Y, Wang J 2020 J. Phys. Chem. Lett. 11 3920Google Scholar

    [17]

    Lu S, Zhou Q, Guo Y, Wang J 2022 Chem 8 769Google Scholar

    [18]

    Lu S, Zhou Q, Guo Y, Zhang Y, Wu Y, Wang J 2020 Adv. Mater. 32 2002658Google Scholar

    [19]

    Wu Y, Lu S, Ju M, Zhou Q, Wang J 2021 Nanoscale 13 12250Google Scholar

    [20]

    Goldsmith B R, Esterhuizen J, Liu J, Bartel C J, Sutton C 2018 AlChE J. 64 2311Google 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 2044Google Scholar

    [24]

    Perdew J P, Burke K, Ernzerhof M 1996 Phys. Rev. Lett. 77 3865Google Scholar

    [25]

    Cai B, Chen X, Xie M, Zhang S, Liu X, Yang J, Zhou W, Guo S, Zeng H 2018 Mater. Horiz. 5 961Google Scholar

    [26]

    Blöchl P E 1994 Phys. Rev. B 50 17953Google Scholar

    [27]

    Monkhorst H J, Pack J D 1976 Phys. Rev. B 13 5188Google Scholar

    [28]

    Aryasetiawan F, Karlsson K, Jepsen O, Schönberger U 2006 Phys. Rev. B 74 125106Google Scholar

    [29]

    Becke A D 1993 J. Chem. Phys. 98 1372Google 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 218Google Scholar

    [31]

    Saal J E, Kirklin S, Aykol M, Meredig B, Wolverton C 2013 JOM 65 1501Google 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 011002Google Scholar

    [33]

    Goldschmidt V M 1926 Naturwissenschaften 14 477Google Scholar

    [34]

    Sun Q, Yin W 2017 J. Am. Chem. Soc. 139 14905Google Scholar

    [35]

    Bartel C J, Sutton C, Goldsmith B R, Ouyang R, Musgrave C B, Ghiringhelli L M, Scheffler M 2019 Sci. Adv. 5 eaav0693Google Scholar

    [36]

    Weng B, Song Z, Zhu R, Yan Q, Sun Q, Grice C G, Yan Y, Yin W 2020 Nat. Commun. 11 3513Google Scholar

    [37]

    Filip-Marina R, Giustino F 2018 Proc. Natl. Acad. Sci. U. S. A. 115 5397Google Scholar

    [38]

    Ye W, Chen C, Dwaraknath S, Jain A, Ong S P, Persson K A 2018 MRS Bull. 43 664Google 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 2630Google Scholar

    [40]

    Lu S, Zhou Q, Ma L, Guo Y, Wang J 2019 Small Methods 3 1900360Google Scholar

    [41]

    Goodenough J B 2004 Rep. Prog. Phys. 67 1915Google Scholar

    [42]

    Okada S, Ohzeki M, Taguchi S 2019 Sci. Rep. 9 13036Google Scholar

    [43]

    Wahl R, Vogtenhuber D, Kresse G 2008 Phys. Rev. B 78 104116Google Scholar

    [44]

    Liu P, Nisar J, Pathak B, Ahuja R 2012 Int. J. Hydrogen Energy 37 11611Google 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 364Google Scholar

    [47]

    Kudo A, Hijii S 1999 Chem. Lett. 28 1103Google Scholar

    [48]

    Kudo A, Miseki Y 2009 Chem. Soc. Rev. 38 253Google Scholar

    [49]

    Acar C, Dincer I, Naterer G F 2016 Int. J. Energy Res. 40 1449Google 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 1801428Google Scholar

    [51]

    Greiner M T, Helander M G, Tang W, Wang Z B, Qiu J, Lu Z 2012 Nat. Mater. 11 76Google 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 6914Google Scholar

  • 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.

    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.

    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.

    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.

    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.

    表 A1  过渡金属计算时附加的Hubbard U

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

    元素U
    V3.25
    Mo4.38
    W6.2
    Ni6.2
    Mn3.9
    Fe5.3
    Cr3.7
    Co3.32
    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
    Na原子数
    Ra原子半径
    Va原子体积
    Rcov共价半径
    Ea电子亲合能
    Ne电子数
    χ鲍林电负性
    Hf形成热
    Nm门捷列夫数
    Np周期
    Rion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.

    FormulaEg-PBE /eVEg-HSE /eVBandgap
    Sr2GaSbO61.372.80indirect
    Sr2InSbO61.633.07indirect
    K2NbTaO61.773.06direct
    DownLoad: CSV
  • [1]

    Dorian J P, Franssen H T, Simbeck D R 2006 Energy Policy 34 1984Google Scholar

    [2]

    Omer A M 2008 Renew. Sust. Energ. Rev. 12 2265Google Scholar

    [3]

    Pfenninger S, Hawkes A, Keirstead J 2014 Renew. Sust. Energ. Rev. 33 74Google 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 054043Google 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 10Google Scholar

    [6]

    Chen S, Takata T, Domen K 2017 Nat. Rev. Mater. 2 17050Google Scholar

    [7]

    Hisatomi T, Kubota J, Domen K 2014 Chem. Soc. Rev. 43 7520Google Scholar

    [8]

    Maeda K, Domen K 2010 J. Phys. Chem. Lett. 1 2655Google Scholar

    [9]

    Kumar A, Kumar A, Krishnan V 2020 ACS Catal. 10 10253Google Scholar

    [10]

    Peña M A, Fierro J L G 2001 Chem. Rev. 101 1981Google 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 2275Google 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 2439Google Scholar

    [13]

    Yin W, Weng B, Ge J, Sun Q, Li Z, Yan Y 2019 Energy Environ. Sci. 12 442Google Scholar

    [14]

    Sun H, Xu X, Song Y, Zhou W, Shao Z 2021 Adv. Funct. Mater. 31 2009779Google 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 014435Google Scholar

    [16]

    Zhou Q, Lu S, Wu Y, Wang J 2020 J. Phys. Chem. Lett. 11 3920Google Scholar

    [17]

    Lu S, Zhou Q, Guo Y, Wang J 2022 Chem 8 769Google Scholar

    [18]

    Lu S, Zhou Q, Guo Y, Zhang Y, Wu Y, Wang J 2020 Adv. Mater. 32 2002658Google Scholar

    [19]

    Wu Y, Lu S, Ju M, Zhou Q, Wang J 2021 Nanoscale 13 12250Google Scholar

    [20]

    Goldsmith B R, Esterhuizen J, Liu J, Bartel C J, Sutton C 2018 AlChE J. 64 2311Google 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 2044Google Scholar

    [24]

    Perdew J P, Burke K, Ernzerhof M 1996 Phys. Rev. Lett. 77 3865Google Scholar

    [25]

    Cai B, Chen X, Xie M, Zhang S, Liu X, Yang J, Zhou W, Guo S, Zeng H 2018 Mater. Horiz. 5 961Google Scholar

    [26]

    Blöchl P E 1994 Phys. Rev. B 50 17953Google Scholar

    [27]

    Monkhorst H J, Pack J D 1976 Phys. Rev. B 13 5188Google Scholar

    [28]

    Aryasetiawan F, Karlsson K, Jepsen O, Schönberger U 2006 Phys. Rev. B 74 125106Google Scholar

    [29]

    Becke A D 1993 J. Chem. Phys. 98 1372Google 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 218Google Scholar

    [31]

    Saal J E, Kirklin S, Aykol M, Meredig B, Wolverton C 2013 JOM 65 1501Google 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 011002Google Scholar

    [33]

    Goldschmidt V M 1926 Naturwissenschaften 14 477Google Scholar

    [34]

    Sun Q, Yin W 2017 J. Am. Chem. Soc. 139 14905Google Scholar

    [35]

    Bartel C J, Sutton C, Goldsmith B R, Ouyang R, Musgrave C B, Ghiringhelli L M, Scheffler M 2019 Sci. Adv. 5 eaav0693Google Scholar

    [36]

    Weng B, Song Z, Zhu R, Yan Q, Sun Q, Grice C G, Yan Y, Yin W 2020 Nat. Commun. 11 3513Google Scholar

    [37]

    Filip-Marina R, Giustino F 2018 Proc. Natl. Acad. Sci. U. S. A. 115 5397Google Scholar

    [38]

    Ye W, Chen C, Dwaraknath S, Jain A, Ong S P, Persson K A 2018 MRS Bull. 43 664Google 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 2630Google Scholar

    [40]

    Lu S, Zhou Q, Ma L, Guo Y, Wang J 2019 Small Methods 3 1900360Google Scholar

    [41]

    Goodenough J B 2004 Rep. Prog. Phys. 67 1915Google Scholar

    [42]

    Okada S, Ohzeki M, Taguchi S 2019 Sci. Rep. 9 13036Google Scholar

    [43]

    Wahl R, Vogtenhuber D, Kresse G 2008 Phys. Rev. B 78 104116Google Scholar

    [44]

    Liu P, Nisar J, Pathak B, Ahuja R 2012 Int. J. Hydrogen Energy 37 11611Google 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 364Google Scholar

    [47]

    Kudo A, Hijii S 1999 Chem. Lett. 28 1103Google Scholar

    [48]

    Kudo A, Miseki Y 2009 Chem. Soc. Rev. 38 253Google Scholar

    [49]

    Acar C, Dincer I, Naterer G F 2016 Int. J. Energy Res. 40 1449Google 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 1801428Google Scholar

    [51]

    Greiner M T, Helander M G, Tang W, Wang Z B, Qiu J, Lu Z 2012 Nat. Mater. 11 76Google 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 6914Google Scholar

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Metrics
  • Abstract views:  10615
  • PDF Downloads:  410
  • Cited By: 0
Publishing process
  • Received Date:  01 April 2022
  • Accepted Date:  11 May 2022
  • Available Online:  24 August 2022
  • Published Online:  05 September 2022
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