.. sidebar::

    .. list-table:: **Ontology Card**
       :header-rows: 0

       * - **Domain**
         - Materials Science and Engineering
       * - **Category**
         - Materials Science
       * - **Current Version**
         - 0.0.1
       * - **Last Updated**
         - None
       * - **Creator**
         - Casper Welzel Andersen, Simon Clark
       * - **License**
         - Creative Commons license Attribution 4.0 International (CC BY 4.0)
       * - **Format**
         - ttl
       * - **Download**
         - `Download EMMO Domain Ontology for Photovoltaics (Photovoltaics) <https://github.com/emmo-repo/domain-photovoltaics>`_

EMMO Domain Ontology for Photovoltaics (Photovoltaics)
========================================================================================================

The EMMO Domain Ontology for Photovoltaics is a specialized vocabulary built upon the Elementary Multiperspective Material Ontology (EMMO) framework for comprehensive semantic description of perovskite solar cells and photovoltaic systems. It provides structured vocabulary for describing photovoltaic device components including perovskite absorber layers, transport layers, electrodes, and contact materials, along with their properties and functions. The ontology captures essential photovoltaic device concepts such as optical properties (bandgap, absorption coefficients), electrical properties (carrier mobility, recombination), and performance metrics (efficiency, fill factor, open-circuit voltage). Photovoltaics ontology enables standardized annotation of experimental fabrication procedures, characterization techniques, and computational modeling of perovskite solar cells. The ontology facilitates knowledge integration in photovoltaics research by providing EMMO-compliant semantic representations compatible with broader materials science knowledge bases.

**Example Usage**: Annotate a perovskite solar cell research study with Photovoltaics terms describing device configuration (n-i-p or p-i-n), perovskite composition (CsPbI3, MAPbI3), transport materials (TiO2, spiro-OMeTAD), performance parameters (PCE, Voc, Jsc), and fabrication methods (spin-coating, vapor deposition).

Metrics & Statistics
--------------------------

.. tab:: Graph


    .. list-table:: Graph Statistics
        :widths: 50 50
        :header-rows: 0

        * - **Total Nodes**
          - 131
        * - **Total Edges**
          - 281
        * - **Root Nodes**
          - 12
        * - **Leaf Nodes**
          - 48
    ::


.. tab:: Coverage


    .. list-table:: Knowledge Coverage Statistics
        :widths: 50 50
        :header-rows: 0

        * - **Classes**
          - 47
        * - **Individuals**
          - 0
        * - **Properties**
          - 3

    ::

.. tab:: Hierarchy


    .. list-table:: Hierarchical Metrics
        :widths: 50 50
        :header-rows: 0

        * - **Maximum Depth**
          - 1
        * - **Minimum Depth**
          - 0
        * - **Average Depth**
          - 0.40
        * - **Depth Variance**
          - 0.24
    ::


.. tab:: Breadth


    .. list-table:: Breadth Metrics
        :widths: 50 50
        :header-rows: 0

        * - **Maximum Breadth**
          - 12
        * - **Minimum Breadth**
          - 8
        * - **Average Breadth**
          - 10.00
        * - **Breadth Variance**
          - 4.00
    ::

.. tab:: LLMs4OL


    .. list-table:: LLMs4OL Dataset Statistics
        :widths: 50 50
        :header-rows: 0

        * - **Term Types**
          - 0
        * - **Taxonomic Relations**
          - 46
        * - **Non-taxonomic Relations**
          - 0
        * - **Average Terms per Type**
          - 0.00
    ::

Usage Example
----------------
Use the following code to import this ontology programmatically:

.. code-block:: python

    from ontolearner.ontology import Photovoltaics

    ontology = Photovoltaics()
    ontology.load("path/to/Photovoltaics-ontology.ttl")

    # Extract datasets
    data = ontology.extract()

    # Access specific relations
    term_types = data.term_typings
    taxonomic_relations = data.type_taxonomies
    non_taxonomic_relations = data.type_non_taxonomic_relations