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Battery Cathode Crystal System Dataset
Data Science and Analytics
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About
This dataset contains physical and chemical properties of Li-ion silicate cathodes, which are valuable for predicting the crystal system class of Li-ion batteries. Batteries in this dataset can be classified into three primary crystal system classes: monoclinic, orthorhombic, and triclinic. The dataset is ideal for exploring and applying classification algorithms to determine battery classes.
Columns
- Materials Id: A unique identifier for each material, as listed on materialsproject.org.
- Formula: The chemical formula of the material.
- Spacegroup: Represents the space group of the crystal structure.
- Formation Energy (eV): The energy required for the formation of the material.
- E Above Hull (eV): Describes the energy if the material decomposes into more stable forms.
- Band Gap (eV): The band gap energy of the material in electron volts.
- Nsites: The number of atoms present in the unit cell of the crystal.
- Density (gm/cc): The density of the bulk crystalline material.
- Volume: The unit cell volume of the material.
- Has Bandstructure: A Boolean variable indicating the presence of bandstructure data.
- Crystal System: The target variable, representing the predicted crystal system class, categorised into monoclinic, orthorhombic, and triclinic.
Distribution
The dataset is typically provided as a CSV file, with a sample file named
lithium-ion batteries.csv being 26.95 kB in size. It comprises 11 columns, with all columns containing data for 339 unique entries or records.Usage
This dataset is designed for predicting battery classes using various classification algorithms. It can be used for machine learning models to identify the crystal system of Li-ion batteries based on their properties.
Coverage
The sources do not specify the geographic, time range, or demographic scope of this dataset.
License
CC0: Public Domain
Who Can Use It
Researchers, data scientists, and engineers interested in materials science, battery technology, or applying machine learning for classification problems will find this dataset useful. It is particularly relevant for those aiming to predict or understand the crystal system of Li-ion batteries.
Dataset Name Suggestions
- Li-ion Silicate Crystal System Properties
- Lithium-ion Battery Crystal Classification
- Battery Cathode Crystal System Dataset
- Materials Project Li-ion Silicate Data
Attributes
Original Data Source:Battery Cathode Crystal System Dataset
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