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mendeleev

repository·master·Indexed 43 days ago

https://github.com/lmmentel/mendeleev

A Pythonic periodic table of elements providing a convenient API for accessing properties of elements, ions, and isotopes. Version v1.3.0 integrates with pint for unit-aware physical properties, pandas for data analysis, and bokeh, plotly, or seaborn for periodic table visualizations.

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What's inside mendeleev

  1. Overview of mendeleev capabilities

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    mendeleev is a Pythonic periodic table of elements. It provides a convenient API for accessing properties of elements, ions, and isotopes.

    Key features include:

    • Data Access: Comprehensive properties including basic, physical, computed, and isotope-specific data.
    • Data Science Integration: Easy interface to pandas for data manipulation.
    • Visualization: Integration with bokeh to create customized periodic tables and visualize periodic trends.
    • Units Support: Since v1.3.0, it integrates with pint to provide unit-aware physical properties.

  2. Key features of mendeleev

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    mendeleev provides a comprehensive toolkit for chemical data:

    • Rich Dataset: Over 100 properties per element (atomic, physical, thermodynamic, electronic).
    • Isotope Information: Data on abundances, masses, half-lives, decay modes, and nuclear properties.
    • Units Support: Integration with pint for unit-aware calculations (available since v1.1.0).
    • Visualization: Compatible with bokeh, plotly, and seaborn for creating periodic tables and trends.
    • Data Access: Support for direct SQLite queries, pandas DataFrame exports, and SQLAlchemy ORM.
    • Electronegativity: Access to 14+ different scales (e.g., Pauling, Allred-Rochow, Mulliken).

  3. Explore the mendeleev API structure

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    The mendeleev library is organized into several functional layers. Most users should interact with the Core Modules for high-level data access. Advanced users can leverage Data Models (SQLAlchemy-based), Specialized Modules for calculations, or Visualization tools for rendering periodic tables.

    API Layers

    • Core Modules: High-level functions and classes for accessing element data. Includes mendeleev.mendeleev, mendeleev.fetch, and mendeleev.ion.
    • Data Models: SQLAlchemy models representing chemical entities and properties (mendeleev.models).
    • Specialized Modules: Modules for specific calculations and data processing, such as mendeleev.electronegativity and mendeleev.econf.
    • Utility Modules: Database access and utility functions (mendeleev.db, mendeleev.utils, mendeleev.cli).
    • Visualization: Tools for visualizing periodic tables. The primary entry point is the mendeleev.vis.periodictable.periodic_table function, with support for bokeh, plotly, and seaborn backends.

  5. Available data models in mendeleev

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    The mendeleev library provides several specialized classes to access different types of chemical and physical data. The primary models include:

    • Element: Core elemental data.
    • Isotope: Data related to specific isotopes.
    • IsotopeDecayMode: Information on how isotopes decay.
    • ScatteringFactor: Atomic scattering factors.
    • IonizationEnergy: Ionization energy data.
    • IonicRadius: Ionic radii information.
    • OxidationState: Data on oxidation states.
    • PhaseTransition: Information on phase transitions.
    • ScreeningConstant: Screening constant data.

  7. Understand data origins and conventions for Mendeleev properties

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    The mendeleev package aggregates chemical data from various scientific sources. When using the library, be aware of the following data conventions:

    Density

    • Solids and Liquids: Reported in $g/cm^3$ at temperatures near room temperature.
    • Gases: Calculated ideal gas densities at 25°C and 101.325 kPa (converted from $g/L$ to $g/cm^3$).
    • Allotropes: For elements with multiple allotropes, the most abundant form is reported (e.g., Carbon as graphite, Sulfur as rhombic).

    Atomic Weight

    • Primarily sourced from IUPAC. For radioactive elements, the weight is taken as the mass number of the most stable isotope.

    Electronegativity

    • Allen Scale: Values are reported in eV (not converted to Pauling units).
    • Mullay Scale: For elements with multiple values, specific hybrid values are chosen (e.g., C sp3, N p, O p).

    Radii

    • Covalent Radius (Cordero): For elements like C, values for different valences are averaged. For Mn, Fe, and Co, low and high spin values are averaged.
    • Van der Waals Radius (Alvarez): Bulk data from Alvarez (2013), with noble gas updates from Vogt (2014).

  8. Access raw mendeleev data in multiple formats

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    If you require the raw data instead of using the Python API, you can find the data assets in the mendeleev-data repository. The data is available in the following formats:

    • csv
    • html
    • json
    • sql
    • markdown

    All data assets are tagged with the same version numbers as the mendeleev package.

  9. Use Element, Isotope, and Ion data models

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    mendeleev provides object-oriented representations of chemical entities.

    Core Models

    • Element: The main class with 80+ properties (e.g., atomic_number, atomic_weight, melting_point). Accessed via element().
    • Isotope: Contains mass, abundance, half-life, and decay modes. Accessed via isotope() or element.isotopes.
    • Ion: Represents ionic species with charge-dependent properties. Created via Ion('Symbol', charge=N).

    Property Models

    • IonicRadius: Radii for different oxidation states and coordination numbers.
    • IonizationEnergy: Successive ionization energies.
    • OxidationState: Possible oxidation states for elements.
    • PropertyMetadata: Metadata about properties including units, sources, and citations.

  11. Set up mendeleev for development

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    For contributors needing to run tests, build documentation, or modify the codebase, follow these steps using poetry:

    1. Clone the repository and enter the directory.
    2. Install core dependencies.
    3. (Optional) Install visualization extras.
    4. (Optional) Install pre-commit hooks for ruff linting and formatting.
    5. Run the test suite using pytest.
    6. (Optional) Build documentation by installing docs dependencies and running make html.
    git clone https://github.com/lmmentel/mendeleev.git
cd mendeleev

# Install core dependencies
poetry install

# With visualization extras
poetry install --with vis

# Install pre-commit hooks (ruff lint + format)
pre-commit install

# Run tests
poetry run pytest

# Build docs (one-time: install docs deps, then build)
poetry install --with docs
cd docs
poetry run make html

  12. Verify data sources for property values

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    If you suspect a property value is incorrect, you can check the citation keys associated with that attribute by querying the propertymetadata table.

    from mendeleev import fetch_table

metadata = fetch_table('propertymetadata')
prop_info = metadata[metadata['attribute_name'] == 'atomic_radius']
print(prop_info['citation_keys'])