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，现有模型通常以牺牲精度或效率为代价。此外，轻量化模型因能以更低计算成本模拟更大规模系统而备受青睐。一个世纪前，费利克斯·布洛赫展示了利用晶格平移操作（具有几何对称性）的等变性如何能显著降低确定波函数的计算成本并精确计算材料性质。本文提出了一种轻量等变交互图神经网络（LEIGNN），可在晶体中实现精准高效的原子间势与力预测。LEIGNN不依赖高阶表示，而是采用标量-矢量双表示编码等变特征。通过从矢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，借助等变性确保预测精度与鲁棒性。结果表明，LEIGNN在催化剂、分子和有机异构体等多个数据集上的预测性能始终优于代表性基线模型，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，我们在固态、液态和气态等不同系统上进行了经典分子动力学（MD）和从头算MD模拟。研究发现，LEIGNN在所有测试系统中既能达到从头算MD的精度，又能保持经典MD的计算效率，充分证明了其精确性、高效性与普适性。