Molecular Potential Energy Computation via Graph Edge Aggregate Attention Neural Network

Accurate potential energy surface (PES) calculation is the basis of molecular dynamics research. Using deep learning (DL) methods can improve the speed of PES calculation while achieving competitive accuracy to ab initio methods. However, the performance of DL model is extremely sensitive to the distribution of training data. Without sufficient training data, the DL model suffers from overfitting issues that lead to catastrophic performance degradation on unseen samples. To solve this problem, based on the message passing paradigm of graph neural networks, we innovatively propose an edge-aggregate-attention mechanism, which specifies the weight based on node and edge information. Experiments on MD17 and QM9 datasets show that our model not only achieves higher PES calculation accuracy but also has better generalization ability compared with Schnet, which demonstrates that edge-aggregate-attention can better capture the inherent features of equilibrium and non-equilibrium molecular conformations.