crystIT

crystIT computes information-theoretic complexity measures of crystal structures from CIF (Crystallographic Information File) data using Python, explicitly accounting for partial occupancies.

Key Features:

• Information-theoretic measures: Implements information theory–based metrics to quantify structural complexity of crystal structures.
• CIF parsing and data extraction: Processes CIF files to extract atomic and crystallographic data required for complexity calculations.
• Partial-occupancy handling: Incorporates updated/refined formulas to accommodate structures with partial occupancies common in disordered systems.
• Support for ordered and disordered systems: Enables computation of complexity measures for both ordered and disordered crystal structures.

Scientific Applications:

• Complexity analysis: Quantifies the informational complexity of crystal structures to compare and rank structural intricacy.
• Material-properties investigation: Provides measures that can aid interpretation of material properties and behaviors linked to structural complexity.
• Theory development: Supplies refined theoretical metrics for researchers developing or evaluating information-theoretic frameworks for crystallography.

Methodology:

crystIT processes CIF files to extract structural data and applies refined information-theory formulas to calculate complexity measures, explicitly treating partial occupancies in ordered and disordered systems.