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The simplest case of a surface charge distribution is the uniformly charged disk. Calculating its electric field also helps us calculate the electric field of a large plane of charge.
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A representation-independent electronic charge density database for crystalline materials.

Jimmy-Xuan Shen1,2, Jason M Munro3, Matthew K Horton1,4

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|October 28, 2022
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This study introduces a comprehensive database of charge densities for materials science. It provides tools to transform charge density data, enabling advanced machine learning applications.

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Area of Science:

  • Materials Science
  • Computational Chemistry
  • Data Science

Background:

  • Charge density is fundamental in density-functional theory and crucial for materials analysis.
  • It offers rich electronic information, valuable for understanding material properties.
  • Machine learning is increasingly applied in materials science, requiring robust data.

Purpose of the Study:

  • To present a user-friendly, continuously updated database of charge densities.
  • To provide theoretical and computational tools for charge density representation transformation.
  • To facilitate advanced machine learning studies in materials science.

Main Methods:

  • Development of a modern, accessible interface for accessing charge density data.
  • Implementation of algorithms for transforming charge density representations.
  • Integration with the Materials Project for a large, updated collection.

Main Results:

  • A comprehensive and accessible database of charge densities is now available.
  • Tools for manipulating charge density representations are provided.
  • The database and tools are designed to support machine learning research.

Conclusions:

  • The new charge density database and associated tools will accelerate materials science research.
  • It lowers the barrier for applying machine learning to electronic structure data.
  • This resource is expected to foster new discoveries in materials design and analysis.