A representation-independent electronic charge density database for crystalline materials.
Jimmy-Xuan Shen1,2, Jason M Munro3, Matthew K Horton1,4
1Department of Materials Science and Engineering, University of California, Berkeley, Berkeley, California, 94720, USA.
Scientific Data
|October 28, 2022
Summary
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.
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.
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