The Development of New Perovskite-Type Oxygen Transport Membranes Using Machine Learning

被引:7
作者
Schlenz, Hartmut [1 ,2 ,3 ]
Baumann, Stefan [1 ,3 ]
Meulenberg, Wilhelm Albert [1 ,3 ,4 ]
Guillon, Olivier [1 ,3 ,5 ]
机构
[1] Forschungszentrum Juelich, IEK 1 Mat Synth & Proc, Inst Energy & Climate Res IEK, Wilhelm Johnen Str, D-52425 Julich, Germany
[2] Univ Bonn, Inst Geosci, Div Geochem & Petrol, Meckenheimer Allee 139, D-53115 Bonn, Germany
[3] Juelich Aachen Res Alliance JARA Energy, D-52425 Julich, Germany
[4] Univ Twente, Fac Sci & Technol, Inorgan Membranes, POB 217, NL-7500 AE Enschede, Netherlands
[5] Rhein Westfal TH Aachen, Inst Mineral Engn, Dept Ceram & Refractory Mat, D-52064 Aachen, Germany
关键词
ceramic; perovskite; oxygen separation membrane; mixed ionic-electronic conducting membrane MIEC; valence bond calculations; machine learning; !text type='python']python[!/text] programming; Pecon; py; BOND-VALENCE PARAMETERS; ELECTRICAL-CONDUCTIVITY; SOLID ELECTROLYTES; IONIC-CONDUCTIVITY; FUEL-CELL; SRTIO3; PREDICTION; OXIDES; STABILITY; VACANCIES;
D O I
10.3390/cryst12070947
中图分类号
O7 [晶体学];
学科分类号
0702 ; 070205 ; 0703 ; 080501 ;
摘要
The aim of this work is to predict suitable chemical compositions for the development of new ceramic oxygen gas separation membranes, avoiding doping with toxic cobalt or expensive rare earths. For this purpose, we have chosen the system Sr<INF>1-x</INF>Ba<INF>x</INF>(Ti<INF>1-y-z</INF>V<INF>y</INF>Fe<INF>z</INF>)O<INF>3-delta</INF> (cubic perovskite-type phases). We have evaluated available experimental data, determined missing crystallographic information using bond-valence modeling and programmed a Python code to be able to generate training data sets for property predictions using machine learning. Indeed, suitable compositions of cubic perovskite-type phases can be predicted in this way, allowing for larger electronic conductivities of up to sigma<INF>e</INF> = 1.6 S/cm and oxygen conductivities of up to sigma<INF>i</INF> = 0.008 S/cm at T = 1173 K and an oxygen partial pressure p<INF>O<INF>2</INF></INF> = 10-15 bar, thus enabling practical applications.
引用
收藏
页数:17
相关论文
共 78 条
  • [1] AHA DW, 1991, MACH LEARN, V6, P37, DOI 10.1007/BF00153759
  • [2] Artificial Neural Network Modeling to Predict the Effect of Milling Time and TiC Content on the Crystallite Size and Lattice Strain of Al7075-TiC Composites Fabricated by Powder Metallurgy
    Alam, Mohammad Azad
    Ya, Hamdan H.
    Azeem, Mohammad
    Yusuf, Mohammad
    Soomro, Imtiaz Ali
    Masood, Faisal
    Shozib, Imtiaz Ahmed
    Sapuan, Salit M.
    Akhter, Javed
    [J]. CRYSTALS, 2022, 12 (03)
  • [3] Defect Genome of Cubic Perovskites for Fuel Cell Applications
    Balachandran, Janakiraman
    Lin, Lianshan
    Anchell, Jonathan S.
    Bridges, Craig A.
    Ganesh, P.
    [J]. JOURNAL OF PHYSICAL CHEMISTRY C, 2017, 121 (48) : 26637 - 26647
  • [4] BOND-VALENCE PARAMETERS FOR SOLIDS
    BRESE, NE
    OKEEFFE, M
    [J]. ACTA CRYSTALLOGRAPHICA SECTION B-STRUCTURAL SCIENCE, 1991, 47 : 192 - 197
  • [5] Brown I.D., 2016, The Chemical Bond in Inorganic Chemistry, V2
  • [6] Brown ID, 2014, STRUCT BOND, V158, P1, DOI 10.1007/978-3-642-54968-7
  • [7] Machine learning for molecular and materials science
    Butler, Keith T.
    Davies, Daniel W.
    Cartwright, Hugh
    Isayev, Olexandr
    Walsh, Aron
    [J]. NATURE, 2018, 559 (7715) : 547 - 555
  • [8] LIBSVM: A Library for Support Vector Machines
    Chang, Chih-Chung
    Lin, Chih-Jen
    [J]. ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY, 2011, 2 (03)
  • [9] A Critical Review of Machine Learning of Energy Materials
    Chen, Chi
    Zuo, Yunxing
    Ye, Weike
    Li, Xiangguo
    Deng, Zhi
    Ong, Shyue Ping
    [J]. ADVANCED ENERGY MATERIALS, 2020, 10 (08)
  • [10] Comparative analysis of machine learning approaches on the prediction of the electronic properties of perovskites: A case study of ABX3 and A2BB'X6
    Chenebuah, Ericsson Tetteh
    Nganbe, Michel
    Tchagang, Alain Beaudelaire
    [J]. MATERIALS TODAY COMMUNICATIONS, 2021, 27