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. Feature...
| Main Authors: | , |
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| Format: | Dataset |
| Language: | unknown |
| Published: |
Zenodo
2024
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| Subjects: | |
| Online Access: | https://dx.doi.org/10.5281/zenodo.11551202 https://zenodo.org/doi/10.5281/zenodo.11551202 |
| _version_ | 1821677593949110272 |
|---|---|
| author | Zheng Yu TheJacksonLab |
| author_facet | Zheng Yu TheJacksonLab |
| author_sort | Zheng Yu |
| collection | DataCite |
| description | 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. ... |
| format | Dataset |
| genre | Orca |
| genre_facet | Orca |
| id | ftdatacite:10.5281/zenodo.11551202 |
| institution | Open Polar |
| language | unknown |
| op_collection_id | ftdatacite |
| op_doi | https://doi.org/10.5281/zenodo.1155120210.5281/zenodo.1217016410.5281/zenodo.11551203 |
| op_relation | https://github.com/TheJacksonLab/gECG_thiophene/tree/v1.0.0 https://github.com/TheJacksonLab/gECG_thiophene/tree/v1.0.0 https://dx.doi.org/10.5281/zenodo.12170164 https://dx.doi.org/10.5281/zenodo.11551203 |
| op_rights | Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 |
| publishDate | 2024 |
| publisher | Zenodo |
| record_format | openpolar |
| spelling | ftdatacite:10.5281/zenodo.11551202 2025-01-17T00:09:48+00:00 TheJacksonLab/gECG_thiophene: v1.0.1 ... Zheng Yu TheJacksonLab 2024 https://dx.doi.org/10.5281/zenodo.11551202 https://zenodo.org/doi/10.5281/zenodo.11551202 unknown Zenodo https://github.com/TheJacksonLab/gECG_thiophene/tree/v1.0.0 https://github.com/TheJacksonLab/gECG_thiophene/tree/v1.0.0 https://dx.doi.org/10.5281/zenodo.12170164 https://dx.doi.org/10.5281/zenodo.11551203 Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 Dataset dataset 2024 ftdatacite https://doi.org/10.5281/zenodo.1155120210.5281/zenodo.1217016410.5281/zenodo.11551203 2024-07-03T12:28:32Z 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. ... Dataset Orca DataCite |
| spellingShingle | Zheng Yu TheJacksonLab TheJacksonLab/gECG_thiophene: v1.0.1 ... |
| title | TheJacksonLab/gECG_thiophene: v1.0.1 ... |
| title_full | TheJacksonLab/gECG_thiophene: v1.0.1 ... |
| title_fullStr | TheJacksonLab/gECG_thiophene: v1.0.1 ... |
| title_full_unstemmed | TheJacksonLab/gECG_thiophene: v1.0.1 ... |
| title_short | TheJacksonLab/gECG_thiophene: v1.0.1 ... |
| title_sort | thejacksonlab/gecg_thiophene: v1.0.1 ... |
| url | https://dx.doi.org/10.5281/zenodo.11551202 https://zenodo.org/doi/10.5281/zenodo.11551202 |