@inproceedings{
wang2024neural,
title={Neural P\${\textasciicircum}3\$M: A Long-Range Interaction Modeling Enhancer for Geometric {GNN}s},
author={Yusong Wang and Chaoran Cheng and Shaoning Li and Yuxuan Ren and Bin Shao and Ge Liu and Pheng-Ann Heng and Nanning Zheng},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=ncqauwSyl5}
}
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. Codes are available at https://github.com/OnlyLoveKFC/Neural_P3M.
Neural P^3M: A Long-Range Interaction Modeling Enhancer for Geometric GNNs
Yusong Wang Chaoran Cheng Shaoning Li Yuxuan Ren Bin Shao Ge Liu Pheng-Ann Heng Nanning Zheng