In modern computational materials science, deep learning has shown the capability to predict interatomic potentials, thereby supporting and accelerating conventional simulations. However, existing models typically sacrifice either accuracy or efficiency. Moreover, lightweight models are highly demanded for offering simulating systems on a considerably larger scale at reduced computational costs. A century ago, Felix Bloch demonstrated how leveraging the equivariance of the translation operation on a crystal lattice (with geometric symmetry) could significantly reduce the computational cost of determining wavefunctions and accurately calculate material properties. Here, we introduce a lightweight equivariant interaction graph neural network (LEIGNN) that can enable accurate and efficient interatomic potential and force predictions in crystals. Rather than relying on higher-order representations, LEIGNN employs a scalar-vector dual representation to encode equivariant features. By extracting both local and global structures from vector representations and learning geometric symmetry information, our model remains lightweight while ensuring prediction accuracy and robustness through the equivariance. Our results show that LEIGNN consistently outperforms the prediction performance of the representative baselines and achieves significant efficiency across diverse datasets, which include catalysts, molecules, and organic isomers. Finally, to further validate the predicted interatomic potentials from our model, we conduct classical molecular dynamics (MD) and ab initio MD simulation across various systems, including solid, liquid, and gas. It is found that LEIGNN can achieve the accuracy of ab initio MD and retain the computational efficiency of classical MD across all examined systems, demonstrating its accuracy, efficiency, and universality.

翻译：在现代计算材料科学中，深度学习已展现出预测原子间势能的能力，从而支持并加速传统模拟。然而，现有模型通常在精度与效率之间进行取舍，且亟需轻量级模型以更低计算成本模拟更大规模系统。一个世纪前，菲利克斯·布洛赫已证明：利用晶格平移操作（具有几何对称性）的等变性，可显著降低波函数求解的计算成本并精确计算材料性质。本文提出一种轻量等变交互图神经网络（LEIGNN），能够实现晶体中原子间势能与力的准确高效预测。不同于依赖高阶表示的方法，LEIGNN采用标量-向量双表示编码等变特征。通过从向量表示中提取局部与全局结构并学习几何对称信息，模型在保持轻量化的同时，借助等变性确保预测精度与鲁棒性。实验结果表明，LEIGNN在催化剂、分子及有机异构体等多类数据集上持续优于代表性基线模型的预测性能，并实现显著的计算效率提升。最后，为验证模型预测的原子间势能，我们在固体、液体及气体等不同体系中进行经典分子动力学（MD）与从头算分子动力学模拟。研究发现，LEIGNN在所有检测体系中既能达到从头算MD的精度，又可保持经典MD的计算效率，充分展现了其准确性、高效性与普适性。