Materials Project Computed Properties Dataset

Welcome to the Dataset!

Dive into the fascinating world of materials science with the Materials Project Computed Properties Dataset! This comprehensive collection features computed properties for a vast array of materials, sourced from the renowned Materials Project database. Whether you’re a researcher exploring new materials, a data scientist building predictive models, or a student curious about how materials behave, this dataset is your gateway to discovering the secrets of matter at the atomic level.

Context

Materials science is all about understanding how the structure of materials—like metals, ceramics, or semiconductors—determines their properties and potential applications. The Materials Project uses advanced computational techniques, specifically density functional theory (DFT), to predict how materials behave without needing to synthesize them in a lab. This dataset brings those predictions to you, offering a treasure trove of chemical and physical properties for thousands of materials. It’s a powerful resource for accelerating materials discovery, from designing better batteries to creating stronger alloys.

Dataset Description

Content

The dataset contains entries for a wide range of unique materials, each identified by a material_id. Every entry includes detailed information about the material’s chemical composition and physical properties, calculated using DFT. Key features typically include:

• material_id: Unique identifier for each material in the Materials Project database.
• formula: Chemical formula of the material (e.g., SiO2, LiFePO4).
• structure: Crystal structure details, such as lattice parameters or atomic positions.
• band_gap: Energy band gap (in eV), indicating whether the material is a conductor, semiconductor, or insulator.
• formation_energy: Energy required to form the material from its constituent elements (in eV/atom).
• density: Material density (in g/cm³).
• total_energy: Total energy of the material’s electronic structure (in eV).
• elastic_properties: Mechanical properties like bulk modulus or shear modulus (if available).
• magnetic_properties: Magnetic moment or type (e.g., ferromagnetic, if applicable).

Note: The exact properties may vary depending on the material and DFT calculation settings. Check the dataset documentation for a full list of features.

Format

• File: Likely stored as a CSV, JSON, or Parquet file (e.g., materials_properties.csv) in the data/ directory.
• Size: Varies based on the number of materials and properties included (assumed to be thousands of entries).

Source

The data is sourced from the Materials Project database, a collaborative effort to compute the properties of known and hypothetical materials using DFT. The dataset is curated for public use, enabling researchers and practitioners to explore material properties without running expensive simulations.

Use Cases

This dataset opens up exciting possibilities for various applications:

• Materials Discovery: Identify new materials with desirable properties, like high band gaps for semiconductors or low formation energies for stable compounds.
• Machine Learning: Train models to predict material properties (e.g., band gap, density) from chemical compositions or structures.
• Data Visualization: Create interactive plots of material properties, such as band gap vs. formation energy, to uncover trends.
• Research: Analyze relationships between chemical composition, crystal structure, and physical properties.
• Education: Use in materials science or data science courses to teach DFT concepts and ML applications.

Acknowledgements

We thank the Materials Project team for making this data publicly available through their database, enabling global research in materials science. The Materials Project is supported by the U.S. Department of Energy and various academic institutions.

For more information about the Materials Project, visit: https://materialsproject.org/

License

Creative Commons Attribution 4.0 International (CC BY 4.0), as specified by the Materials Project database. See https://materialsproject.org/about for license details.

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