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 ### Highlighted features
 
 - **interfaced with multiple backends**, including TensorFlow, PyTorch, and JAX, the most popular deep learning frameworks, making the training process highly automatic and efficient.
-- **interfaced with high-performance classical MD and quantum (path-integral) MD packages**, including LAMMPS, i-PI, AMBER, CP2K, GROMACS, OpenMM, and ABUCUS.
+- **interfaced with high-performance classical MD and quantum (path-integral) MD packages**, including LAMMPS, i-PI, AMBER, CP2K, GROMACS, OpenMM, and ABACUS.
 - **implements the Deep Potential series models**, which have been successfully applied to finite and extended systems, including organic molecules, metals, semiconductors, insulators, etc.
 - **implements MPI and GPU supports**, making it highly efficient for high-performance parallel and distributed computing.
 - **highly modularized**, easy to adapt to different descriptors for deep learning-based potential energy models.