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准确性或效率。此外，在降低计算成本的同时，轻量级模型对于模拟更大规模系统具有迫切需求。一个世纪前，费利克斯·布洛赫证明了利用晶格平移操作（具有几何对称性）的等变性可大幅降低确定波函数的计算成本并准确计算材料性质。本文提出一种轻量等变交互图神经网络（LEIGNN），能够对晶体中的原子间势和力进行准确高效的预测。LEIGNN不依赖高阶表示，而是采用标量-向量双重表示来编码等变特征。通过从向量表示中提取局部和全局结构并学习几何对称信息，我们的模型在保持轻量化的同时，借助等变性确保了预测的准确性与鲁棒性。实验结果表明，LEIGNN在多种数据集（包括催化剂、分子和有机异构体）上始终优于代表性基线模型的预测性能，并实现了显著的计算效率。最后，为进一步验证模型预测的原子间势，我们在固体、液体和气体等多种系统中进行了经典分子动力学（MD）和从头算分子动力学模拟。研究发现，LEIGNN在所有检验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，充分证明了其准确性、高效性和普适性。