Neural P$^3$M: A Long-Range Interaction Modeling Enhancer for Geometric GNNs
<|reference_start|>Neural P$^3$M: A Long-Range Interaction Modeling Enhancer for Geometric GNNs: Geometric graph neural networks (GNNs) have emerged as powerful tools for modeling molecular geometry. However, they encounter limitations in effectively capturing long-range interactions in large molecular systems. To address this challenge, we introduce Neural P$^3$M, a versatile enhancer of geometric GNNs to expand the scope of their capabilities by incorporating mesh points alongside atoms and reimaging traditional mathematical operations in a trainable manner. Neural P$^3$M exhibits flexibility across a wide range of molecular systems and demonstrates remarkable accuracy in predicting energies and forces, outperforming on benchmarks such as the MD22 dataset. It also achieves an average improvement of 22% on the OE62 dataset while integrating with various architectures.<|reference_end|>
@article{wang2024neural,
    title={Neural P$^3$M: A Long-Range Interaction Modeling Enhancer for Geometric
  GNNs},
    author={Yusong Wang, Chaoran Cheng, Shaoning Li, Yuxuan Ren, Bin Shao, Ge Liu,
  Pheng-Ann Heng, Nanning Zheng},
    journal={arXiv preprint arXiv:2409.17622},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.17622},
    primaryClass={cs.LG cs.AI}
}