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

Hartmut Schlenz*, Stefan Baumann, Wilhelm Albert Meulenberg, Olivier Guillon

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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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 (Sr1−xBax)(Ti1−y−zVyFez)O3−δ (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 σe = 1.6 S/cm and oxygen conductivities of up to σi = 0.008 S/cm at T = 1173 K and an oxygen partial pressure pO2 = 10−15 bar, thus enabling practical applications.

Original languageEnglish
Article number947
Issue number7
Publication statusPublished - 5 Jul 2022


  • ceramic
  • machine learning
  • mixed ionic-electronic conducting membrane MIEC
  • oxygen separation membrane
  • perovskite
  • python programming
  • valence bond calculations

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