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Materials Database from All-electron Hybrid Functional DFT Calculations.

Akhil S Nair1,2, Lucas Foppa3, Matthias Scheffler3

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Summary
This summary is machine-generated.

This study introduces a new inorganic materials database using advanced hybrid functional calculations. This resource improves the reliability of materials discovery and artificial intelligence (AI) models for predicting material properties.

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Area of Science:

  • Computational Materials Science
  • Materials Informatics
  • Artificial Intelligence in Materials Science

Background:

  • Materials databases are crucial for discovering materials with enhanced properties.
  • Current databases often use generalized gradient approximation (GGA), limiting accuracy for certain materials and properties.
  • This limitation affects the reliability of artificial intelligence (AI) models trained on these databases.

Purpose of the Study:

  • To create a comprehensive database of inorganic materials using more accurate computational methods.
  • To assess the thermodynamic and electrochemical stability of oxides for catalysis and energy applications.
  • To demonstrate the utility of this database for training AI models.

Main Methods:

  • Generation of a database containing 7,024 inorganic materials with diverse structures and compositions.
  • Utilizing hybrid functional calculations implemented in the all-electron code FHI-aims for database creation.
  • Employing the sure-independence screening and sparsifying operator (SISSO) approach for AI model training.

Main Results:

  • A novel database of 7,024 inorganic materials was successfully generated using hybrid functional calculations.
  • The database enables reliable evaluation of thermodynamic and electrochemical stability for oxides.
  • Demonstrated successful application of the database in training AI models for material property prediction.

Conclusions:

  • The new database, built with hybrid functional calculations, enhances the reliability of materials discovery.
  • This resource is valuable for assessing oxide stability in catalysis and energy applications.
  • The database facilitates the development of more accurate AI models for materials science.