基于图边特征聚合注意力神经网络的分子势能计算

Molecular Potential Energy Computation via Graph Edge Aggregate Attention Neural Network

势能面的精准计算是分子动力学研究的基础、使用深度学习方法可以在获得与从头算方法相当精度的同时显著地提高其计算速度.然而,深度学习模型的性能对训练数据的分布极其敏感,在缺乏足够训练数据的情况下,深度学习模型将会出现过拟合问题,从而导致模型在测试样本.上的精度下降.本文基于图神经网络的消息传递范式,创新地提出了一种边聚合注意力机制,在传统图注意力机制仅关注节点信息的基础上,引入分子图中的边信息生成注意力权重,从而增强了模型的表达能力.在MD17和QM9数据集上的一系列实验结果显.示,与基线模型相比,边聚合注意力模型均取得了更高的势能面计算精度和更好的泛化能力,结果表明对于平衡态和非平衡态分子构型,边注意力聚合都能够很好地捕捉其内在特征.

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 MDI7 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.