Crystal Structure Toolkit (CIF Parser & MOL Converter)

A simple Crystallographic Information File (CIF) parser built with Python and Pandas. This script extracts specific loops from CIF files, filters columns based on user preferences, and can search for and rename specific columns (e.g., charge-related columns).

Workflow: The Full Pipeline

This toolkit handles the end-to-end preparation of crystal structures for research:

Step 1: Convert (MOL → CIF)

If your structure is currently in a .mol format (common in Material Studio), use the converter in the mol-to-cif-converter/ folder to generate a base CIF file with fractional coordinates.

python3 mol-to-cif-converter/convert.py SRC_MOL_FILE_HERE DESTINATION_CIF_FILE_HERE

Note: You must manually update the LATTICE_VECTORS variable inside mol-to-cif-converter/convert.py to match your material's specific dimensions before running the script.

Step 2: Parse & Clean (CIF → Standardized CIF)

Once you have your CIF files, use the main parse.py script to strip unnecessary columns and ensure charge labels are correctly formatted for simulation engines.

python3 parse.py

Features

• Format Conversion: Converts Cartesian coordinates from .mol files into Fractional coordinates for .cif.
• Loop Extraction: Specifically targets and modifies a specified loop_ block within CIF files.
• Column Filtering: Retains a set of predefined columns (_atom_site_label, _atom_site_fract_x, etc.).
• Keyword Search: Identifies and renames columns containing specific keywords (e.g., 'charge').
• CSV Export: Generates a summary metal_names.csv mapping filenames to the value of a specific column (e.g., the metal name).
• Flexible Destination: Can export each result into its own subfolder or a single destination directory.

Prerequisites

• Python 3.x
• Check libraries on requirements.txt

Installation

1. Clone this repository or download the parse.py script.
2. Install the required dependencies:

   pip install -r requirements.txt

Configuration

The script is configured through variables at the top of parse.py:

• user_src_folder_path: Directory containing the source .cif files (default: src).
• user_dest_folder_path: Directory where the modified files will be saved (default: dest).
• loop_table_no: The index of the loop_ block you wish to modify (default: 2).
• user_column_to_keep: A list of column labels to keep from the selected loop.
• user_keywords_to_find: A list of keywords to search for in other columns.
• target_keyword_column_name: The new name for any column that matches the keywords.
• extend_dest: If True, exports each processed variation into a separate subfolder (e.g., dest/0/, dest/1/).

Usage

1. Place your .cif files in the source directory (e.g., src/).
2. Adjust the configuration in parse.py if necessary.
3. Run the script:

   python3 parse.py

The processed files will be available in the destination directory (dest/), and a summary metal_names.csv will be created.

Use Cases & Compatibility

This script was originally developed to assist in preparing Crystallographic Information Files for research and thesis work in materials science. It is particularly useful for:

• Molecular Simulations: Standardizing CIF files for use in software like RASPA, Zeo++, or GCMC simulations where clean atomic coordinates and specific charge labels (_atom_site_charge) are required.
• Structure Visualization: Simplifying complex CIFs for easier import into visualization tools like VESTA or Mercury by removing redundant or incompatible loop data.
• Data Pre-processing: Converting raw database exports (e.g., from the CCDC or ICSD) into a consistent format for high-throughput computational screening.

Example

Input (src/example.cif)

loop_
_atom_site_label
_atom_site_type_symbol
_atom_site_fract_x
_atom_site_fract_y
_atom_site_fract_z
_atom_site_U_iso_or_equiv
Cu1 Cu 0.2416(7) 1.07587(27) 0.2876(8) 0.0089
O1 O 0.3507(8) 0.99172(28) 0.0651(10) 0.0089

Output (dest/0/example_0.cif)

loop_
_atom_site_label
_atom_site_fract_x
_atom_site_fract_y
_atom_site_fract_z
Cu1  0.2416(7)  1.07587(27)  0.2876(8)
O1  0.3507(8)  0.99172(28)  0.0651(10)

How it Works

1. Parsing: The script reads the CIF file line by line to identify data_ blocks and loop_ sections.
2. Processing: It uses Pandas to read the table data within the specified loop.
3. Modification: It filters the DataFrame to keep only requested columns and searches for any columns containing the specified keywords.
4. Exporting: It reconstructs the CIF file with the modified table data and saves it to the destination path.

History

This project was originally created 3 years ago to help a lecturer prepare crystal structure data for their thesis. It has been preserved as a lightweight utility for the materials science community.