hamitasneem/Physics-Informed-Discovery-of-Topological-Materials-Using-Descriptor-Graph-Learning-: Topological Materials ML Dataset and Workflow Files (v1.1)

v1.1 — Initial Public Research Release This release provides the complete notebook workflow, supporting dataset archive, and documentation required to reproduce the machine-learning pipeline for topological materials discovery. Added Complete Jupyter notebook workflow (Topological_Files_New.ipynb) Supporting dataset archive (Files.zip) Project documentation (README.md) Conda environment specification (environment.yml) Python dependency list (requirements.txt) MIT License Features Data preprocessing and feature engineering workflow Materials informatics descriptor generation Machine learning pipeline for topological materials prediction Candidate screening and ranking framework Visualization and analysis utilities Reproducible notebook-based workflow Installation Download the repository contents. Download and extract Files.zip. Place the extracted Files directory in the repository root. Create the environment using: conda env create -f environment.yml conda activate topo_ml Open and run: Topological_Files_New.ipynb Notes This release represents the first publicly available version of the workflow and supporting files. The notebook is designed to be executed sequentially from top to bottom and assumes the accompanying Files directory is present in the project root. Author Tasneem U. Rehman PhD Research: Data-Driven Design and Prediction of Spintronic and Topological Materials Using Artificial Intelligence.