TheJacksonLab/gECG_thiophene: v1.0.0
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. Feature...
| Main Authors: | , |
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| Format: | Other/Unknown Material |
| Language: | unknown |
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Zenodo
2024
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| Online Access: | https://doi.org/10.5281/zenodo.11551203 |
| Summary: | 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. |
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