Neural P$^3$M: A Long-Range Interaction Modeling Enhancer for Geometric GNNs

• URL: http://arxiv.org/abs/2409.17622v1
• Date: Thu, 26 Sep 2024 08:16:59 GMT
• Title: Neural P$^3$M: A Long-Range Interaction Modeling Enhancer for Geometric GNNs
• Authors: Yusong Wang, Chaoran Cheng, Shaoning Li, Yuxuan Ren, Bin Shao, Ge Liu, Pheng-Ann Heng, Nanning Zheng
• Abstract summary: We introduce Neural P$3$M, a versatile enhancer of geometric GNNs to expand the scope of their capabilities. It exhibits flexibility across a wide range of molecular systems and demonstrates remarkable accuracy in predicting energies and forces. It also achieves an average improvement of 22% on the OE62 dataset while integrating with various architectures.
• Score: 66.98487644676906
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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