研究目的
To develop a procedure to identify the formability of perovskites for all the compounds with the stoichiometry of ABX3 and (A′A′′)(B′B′′)X6, that exist in experiments and are stored in the database of Materials Projects, and to correct the possible errors of previous data in ABO3 compounds.
研究成果
The study developed a procedure to identify the perovskite formability of ABX3 and A2B′B′′X6 compounds, extending previous perovskite data and correcting mistakes in ABO3 compounds. Machine learning based on the current data achieved high prediction accuracy for perovskite formability, paving the way for reliable machine learning work in the future.
研究不足
The validity of data cannot be fully guaranteed due to unavoidable human and measurement mistakes in ICSD. The study also identified compounds with suspicious formability results that require further experimental validation.
1：Experimental Design and Method Selection:
A procedure was developed to identify the formability of perovskites based on crystal structures stored in the Materials Project database. The criteria for perovskite identification included cation coordination and topology of corner-sharing octahedral structure.
2：Sample Selection and Data Sources:
Crystal structures with ICSD numbers from the Materials Project database were selected.
3：List of Experimental Equipment and Materials:
The study utilized computational methods and data from the Materials Project database.
4：Experimental Procedures and Operational Workflow:
The workflow involved collecting crystal structures, calculating cation coordination, identifying A and B sites, and analyzing the stacking topology of B-centered octahedra.
5：Data Analysis Methods:
Machine learning models were used to analyze the data and predict perovskite formability.
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