TheJacksonLab/gECG_thiophene: v1.0.1

Release v1.0.0 - Initial Public Release This is the first official release of gECG_thiophene, which applies machine learning to predict the electronic properties of thiophene polymers. This version provides all necessary tools and documentation to facilitate further development and research. Features: Polymer Data Generation: Scripts to generate polymer datasets from SMILES strings. Molecular Dynamics and QM Calculations: Integration with Lammps and ORCA for sampling conformations and performing precision calculations. gECG Machine Learning Model: Framework for training, inference, and fine-tuning predictive models across different resolutions. Large datasets available on Zenodo for download and immediate use.