Jian Jiang's team at the Institute of Chemistry, Chinese Academy of Sciences, recently published an article that focuses on the core bottlenecks of machine learning force fields (MLFF) in organic systems during long-term molecular dynamics simulations, including molecular structure collapse and low accuracy in macroscopic property calculations, and proposed two physical embedding solutions. Their approach works on two levels, addressing both intramolecular and intermolecular interactions. They develop a physics-guided adaptive bond length sampling method and a top-down model correction method based on physical equation embedding, respectively. The results show that these methods can significantly improve simulation stability under small sample conditions and effectively improve the prediction accuracy of macroscopic properties, such as density and viscosity with extremely low data and computational costs. Their approach effectively overcomes the limitations of purely data-driven methods, significantly enhances the reliability and generalization ability of  MLFF, and provides a scalable approach for physical embedding of MLFF. The article was published as an open access Research Article in CCS Chemistry, the flagship journal of the Chinese Chemical Society.