Graph Self-Supervised Learning for Optoelectronic Properties of Organic Semiconductors

Dec 20, 2021 1 min read

SSL_OSC

Graph Self-Supervised Learning for Optoelectronic Properties of Organic Semiconductors (https://arxiv.org/abs/2112.01633)

GIN/ contains codes for pretraining and finetuning on equilibrium molecules with GIN model.
- GIN/pretrain_masking.py is the code for SSL pretraining and GIN/finetune.py is the code for finetuning.
SchNet/ contains codes for pretraining and finetuning on non-equilibrium molecules with SchNet.
- SchNet/train_ssl.py is the code for SSL pretraining and SchNet/finetune.py is the code for finetuning.

Cite

If you find this repo to be useful, please cite our paper. Thank you.

@article{zhang2021graph,
  title={Graph Self-Supervised Learning for Optoelectronic Properties of Organic Semiconductors},
  author={Zhang, Zaixi and Liu, Qi and Zhang, Shengyu and Hsieh, Chang-Yu and Shi, Liang and Lee, Chee-Kong},
  journal={arXiv preprint arXiv:2112.01633},
  year={2021}
}

GitHub

View Github

Graph Self-Supervised

John was the first writer to have joined pythonawesome.com. He has since then inculcated very effective writing and reviewing culture at pythonawesome which rivals have found impossible to imitate.