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Rationalizing Perovskite Data for Machine Learning and Materials Design.

Qichen Xu1,2, Zhenzhu Li1,2, Miao Liu3

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This study developed a procedure to identify perovskite formability, enriching and correcting existing data for machine learning applications. It highlights compounds needing further experimental validation for reliable materials discovery.

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Area of Science:

  • Materials Science
  • Computational Materials Science
  • Crystallography

Background:

  • Machine learning (ML) is increasingly used for perovskite material design, leveraging extensive formability data.
  • Reliable ML models require accurate and validated datasets to reflect material properties effectively.
  • Existing perovskite formability data may contain inaccuracies, necessitating data curation for trustworthy predictions.

Purpose of the Study:

  • To establish a robust procedure for identifying the formability of perovskite compounds.
  • To curate and enhance the dataset of perovskite formability for ABX3 and (A'A")(B'B'')X6 stoichiometries.
  • To identify perovskite compounds with potentially erroneous formability data for further investigation.

Main Methods:

  • Developed a data validation procedure to assess perovskite formability.
  • Utilized the Materials Projects database for experimental perovskite compound data.
  • Applied a multi-model machine learning approach to analyze formability data.

Main Results:

  • Significantly enriched the available data on perovskite formability.
  • Corrected inaccuracies present in previous datasets for ABO3 compounds.
  • Identified A2B'B"O6 compounds with suspicious formability, requiring experimental validation.

Conclusions:

  • The developed procedure enhances the reliability of perovskite formability data.
  • This work is crucial for advancing accurate machine learning-driven perovskite discovery.
  • Further experimental validation is recommended for identified compounds to ensure data integrity.