Neural P^3M: A Long-Range Interaction Modeling Enhancer for Geometric GNNs - Wang, Yusong; Cheng, Chaoran; Li, Shaoning; Ren, Yuxuan; Shao, Bin; Liu, Ge; Heng, Pheng-Ann; Zheng, Nanning (NeurIPS 2024, 2024)
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^3M, 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^3M 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.