Training Data and Generalizability Testsets

For the publication PhysRevE.101.022502

@article{PhysRevE.101.022502,
  title = {Identifying knot types of polymer conformations by machine learning},
  author = {Vandans, Olafs and Yang, Kaiyuan and Wu, Zhongtao and Dai, Liang},
  journal = {Phys. Rev. E},
  volume = {101},
  issue = {2},
  pages = {022502},
  numpages = {10},
  year = {2020},
  month = {Feb},
  publisher = {American Physical Society},
  doi = {10.1103/PhysRevE.101.022502},
  url = {https://link.aps.org/doi/10.1103/PhysRevE.101.022502}
}

GitHub source code demo using this dataset: 

🥨 https://github.com/CompSoftMatterBiophysics-CityU-HK/Identify-Knot-Types-by-ML-PRE2020

The above GitHub repo provide a docker, training code, best model with weights, and two showcases of generalizability.