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的能力，从而支持并加速传统模拟。然而，现有模型通常牺牲了精度或效率。此外，轻量级模型因能以更低计算成本模拟大规模系统而备受需求。一个世纪前，菲利克斯·布洛赫展示了利用晶格平移操作的等变性（几何对称性）如何能显著降低波函数求解的计算开销，并精确计算材料性质。本文提出一种轻量级等变交互图神经网络（LEIGNN），可在晶体中实现精确高效的原子间势与力预测。LEIGNN不依赖高阶表示，而采用标量-矢量双重表示来编码等变特征。通过从矢量表示中提取局部与全局结构并学习几何对称信息，该模型保持轻量化，同时借助等变性确保预测精度与鲁棒性。实验结果表明，在包含催化剂、分子和有机异构体的多样化数据集上，LEIGNN的预测性能始终优于代表性基线模型，并实现了显著的效率提升。最后，为验证模型预测的原子间势，我们在固体、液体和气体等不同系统中进行了经典分子动力学与从头算分子动力学模拟。研究发现，LEIGNN在所有测试系统中均能达到从头算MD的精度，并保持经典MD的计算效率，展示了其精确性、高效性与普适性。