There are 4 repositories under crystal-structure topic.
Curated list of known efforts in materials informatics = modern materials science
Automatic generation of crystal structure descriptions.
Monte Carlo and Molecular Dynamics Simulation Package
A code for generating irreducible site-occupancy configurations
Data structures, algorithms, and parsing for crystallography
PyQt based GUI tool which allows to visualize, design and export the lattice graph models.
Browser plugin-free CIF visualization: comparison of the open-source engines
A Julia wrapper for the spglib C-API
Ewald summation program for computing the long range Coulomb interactions in 3D Periodic systems
Symmetry crystal combinatorial optimization program for crystal prediction.
Crystallographic files of common materials, elements, oxides, for visualization in Avogadro
This is the proof of concept, how a relatively unsophisticated statistical model trained on the large MPDS dataset predicts physical properties from the only crystalline structure (POSCAR or CIF).
Basic crystallography domain ontology based on EMMO and the CIF core dictionary.
MPDS API client library in Python
An interactive Python script that computes the minimum atomic bonding distances from sites, generating histograms and pair counts.
This package contains some basic functionalities of Crystallography.jl
CMSO - Computational Material Sample Ontology
MatBase is an app that allows you to access and analyze a wide range of material properties and photoelectron spectroscopy parameters.
Identify zone axis from a high res TEM image
An implementation of a genetic algorithm in Python for predicting equilibrium crystal structures for a given potential. The potential implemented here is the Daoud-Cotton model, but this can be easily changed.
A simulation of crystal growth by voronoi diagrams with polygonal growing seeds
POSCAR3D is a 3D visualization tool for POSCAR files, offering realistic atom rendering based on van der Waals radii. Developed for researchers and students, it provides an interactive way to explore molecular structures with ease.
DeepCrysTet: A Deep Learning Approach Using Tetrahedral Mesh for Predicting Properties of Crystalline Materials