搜索

x

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

机器学习加速搜寻新型双钙钛矿氧化物光催化剂

万新阳 章烨辉 陆帅华 吴艺蕾 周跫桦 王金兰

引用本文:
Citation:

机器学习加速搜寻新型双钙钛矿氧化物光催化剂

万新阳, 章烨辉, 陆帅华, 吴艺蕾, 周跫桦, 王金兰

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
PDF
HTML
导出引用
  • A2BB'O6型双钙钛矿氧化物材料, 相比于ABO3型单钙钛矿氧化物材料, 具有更好的稳定性和更宽泛的能带选择范围, 在光催化全解水领域具有良好的应用前景. 然而, 由于晶体结构和组成元素的多样性, 实验和理论上快速、准确搜寻高催化活性的A2BB'O6型双钙钛矿氧化物材料具有相当大的挑战性. 本文由材料数据库的带隙值数据出发, 采用机器学习与第一性原理相结合的方法, 从50000多种A2BB'O6型双钙钛矿氧化物材料中筛选出近8000种可能适用于光催化全解水的材料. 对筛选结果的统计分析表明, B/B'位均为d10金属离子的双钙钛矿氧化物, 更有可能成为全解水光催化剂. 随后通过进一步的第一性原理计算挑选出Sr2GaSbO6, Sr2InSbO6和K2NbTaO6这3种带边位置合适且不含铅、汞离子的A2BB'O6型双钙钛矿氧化物材料作为候选的全解水光催化剂.
    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.
      通信作者: 周跫桦, qh.zhou@seu.edu.cn ; 王金兰, jlwang@seu.edu.cn
    • 基金项目: 国家重点研发计划(批准号: 2017YFA0204800, 2021YFA1500700)、国家自然科学基金(批准号: 22033002, 22003009)和江苏省研究生科研与实践创新计划(批准号: KYCX20_0075)资助的课题.
      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

    Fig. 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

    Fig. 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

    Fig. 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

    Fig. 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

    Fig. 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
    下载: 导出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
    下载: 导出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
    下载: 导出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

  • [1] 郑鹏飞, 柳志旭, 王超, 刘卫芳. 基团替代调控无铅有机钙钛矿铁电体的极化和压电特性的第一性原理研究. 物理学报, 2024, 73(12): 126202. doi: 10.7498/aps.73.20240385
    [2] 张嘉晖. 蛋白质计算中的机器学习. 物理学报, 2024, 73(6): 069301. doi: 10.7498/aps.73.20231618
    [3] 丁莉洁, 张笑天, 郭欣宜, 薛阳, 林常青, 黄丹. SrSnO3作为透明导电氧化物的第一性原理研究. 物理学报, 2023, 72(1): 013101. doi: 10.7498/aps.72.20221544
    [4] 罗启睿, 沈一凡, 罗孟波. 高分子塌缩相变和临界吸附相变的计算机模拟和机器学习. 物理学报, 2023, 72(24): 240502. doi: 10.7498/aps.72.20231058
    [5] 吕程烨, 陈英炜, 谢牧廷, 李雪阳, 于宏宇, 钟阳, 向红军. 外加电磁场下周期性体系的第一性原理计算方法. 物理学报, 2023, 72(23): 237102. doi: 10.7498/aps.72.20231313
    [6] 胡前库, 秦双红, 吴庆华, 李丹丹, 张斌, 袁文凤, 王李波, 周爱国. 三元Nb系和Ta系硼碳化物稳定性和物理性能的第一性原理研究. 物理学报, 2020, 69(11): 116201. doi: 10.7498/aps.69.20200234
    [7] 崔宗杨, 谢忠帅, 汪尧进, 袁国亮, 刘俊明. 钙钛矿铁电半导体的光催化研究现状及其展望. 物理学报, 2020, 69(12): 127706. doi: 10.7498/aps.69.20200287
    [8] 杨自欣, 高章然, 孙晓帆, 蔡宏灵, 张凤鸣, 吴小山. 铅基钙钛矿铁电晶体高临界转变温度的机器学习研究. 物理学报, 2019, 68(21): 210502. doi: 10.7498/aps.68.20190942
    [9] 王逸飞, 李晓薇. 石墨烯/BiOI纳米复合物电子结构和光学性质的第一性原理模拟计算. 物理学报, 2018, 67(11): 116301. doi: 10.7498/aps.67.20172220
    [10] 邵梓桥, 毕恒昌, 谢骁, 万能, 孙立涛. 三氧化钨/氧化银复合材料的水热法合成及其光催化降解性能研究. 物理学报, 2018, 67(16): 167802. doi: 10.7498/aps.67.20180663
    [11] 叶红军, 王大威, 姜志军, 成晟, 魏晓勇. 钙钛矿结构SnTiO3铁电相变的第一性原理研究. 物理学报, 2016, 65(23): 237101. doi: 10.7498/aps.65.237101
    [12] 王平, 郭立新, 杨银堂, 张志勇. 铝氮共掺杂氧化锌纳米管电子结构的第一性原理研究. 物理学报, 2013, 62(5): 056105. doi: 10.7498/aps.62.056105
    [13] 李荣, 罗小玲, 梁国明, 付文升. 掺杂Fe对VH2解氢性能影响的第一性原理研究. 物理学报, 2011, 60(11): 117105. doi: 10.7498/aps.60.117105
    [14] 张学军, 高攀, 柳清菊. 氮铁共掺锐钛矿相TiO2电子结构和光学性质的第一性原理研究. 物理学报, 2010, 59(7): 4930-4938. doi: 10.7498/aps.59.4930
    [15] 吴红丽, 赵新青, 宫声凯. Nb掺杂影响NiTi金属间化合物电子结构的第一性原理计算. 物理学报, 2010, 59(1): 515-520. doi: 10.7498/aps.59.515
    [16] 胡方, 明星, 范厚刚, 陈岗, 王春忠, 魏英进, 黄祖飞. 梯形化合物NaV2O4F电子结构的第一性原理研究. 物理学报, 2009, 58(2): 1173-1178. doi: 10.7498/aps.58.1173
    [17] 宋庆功, 王延峰, 宋庆龙, 康建海, 褚 勇. 插层化合物Ag1/4TiSe2电子结构的第一性原理研究. 物理学报, 2008, 57(12): 7827-7832. doi: 10.7498/aps.57.7827
    [18] 明 星, 范厚刚, 胡 方, 王春忠, 孟 醒, 黄祖飞, 陈 岗. 自旋-Peierls化合物GeCuO3电子结构的第一性原理研究. 物理学报, 2008, 57(4): 2368-2373. doi: 10.7498/aps.57.2368
    [19] 侯清玉, 张 跃, 陈 粤, 尚家香, 谷景华. 锐钛矿(TiO2)半导体的氧空位浓度对导电性能影响的第一性原理计算. 物理学报, 2008, 57(1): 438-442. doi: 10.7498/aps.57.438
    [20] 宋庆功, 姜恩永, 裴海林, 康建海, 郭 英. 插层化合物LixTiS2中Li离子-空位二维有序结构稳定性的第一性原理研究. 物理学报, 2007, 56(8): 4817-4822. doi: 10.7498/aps.56.4817
计量
  • 文章访问数:  5499
  • PDF下载量:  283
  • 被引次数: 0
出版历程
  • 收稿日期:  2022-04-01
  • 修回日期:  2022-05-11
  • 上网日期:  2022-08-24
  • 刊出日期:  2022-09-05

/

返回文章
返回