  Computational simulation of chemical and biological systems using ab initio
molecular dynamics has been a challenge over decades. Researchers have
attempted to address the problem with machine learning and fragmentation-based
methods. However, the two approaches fail to give a satisfactory description of
long-range and many-body interactions, respectively. Inspired by
fragmentation-based methods, we propose the Long-Short-Range Message-Passing
(LSR-MP) framework as a generalization of the existing equivariant graph neural
networks (EGNNs) with the intent to incorporate long-range interactions
efficiently and effectively. We apply the LSR-MP framework to the recently
proposed ViSNet and demonstrate the state-of-the-art results with up to 40% MAE
reduction for molecules in MD22 and Chignolin datasets. Consistent improvements
to various EGNNs will also be discussed to illustrate the general applicability
and robustness of our LSR-MP framework. The code for our experiments and
trained model weights could be found at https://github.com/liyy2/LSR-MP.
