  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.
