ViSNet (shorted for “Vector-Scalar interactive graph neural Network”) is a scalable and accurate graph deep learning potential for molecular dynamics that significantly alleviate the dilemma between computational costs and sufficient utilization of geometric information.
- ViSNet Team won the 2nd place in the OGB-LSC @ NeurIPS 2022 PCQM4Mv2 Track! Please check out the branch OGB-LSC@NIPS2022 and give it a star if you find it useful!
- The paper of ViSNet is under-review. We will release the codebase until it is accepted.
If you find this work useful, please kindly cite following paper:
@article{wang2022visnet,
title={ViSNet: a scalable and accurate geometric deep learning potential for molecular dynamics simulation},
author={Wang, Yusong and Li, Shaoning and He, Xinheng and Li, Mingyu and Wang, Zun and Zheng, Nanning and Shao, Bin and Wang, Tong and Liu, Tie-Yan},
journal={arXiv preprint arXiv:2210.16518},
year={2022}
}Please contact Tong Wang (watong@microsoft.com) for technical support.
This project is licensed under the terms of the MIT license. See LICENSE for additional details.