Notably, most MLIPs are trained and used within the PyTorch framework, which is based on Python. In contrast, SANDER is primarily written in Fortran. Directly calling MLIPs by SANDER could result in high latency due to the frequent initialization of MLIPs in each MD step. Furthermore, Being a high-level language, Python makes direct memory manipulation challenging. To address these issues, we implemented the MLIPs in a built-in manner (see Scheme 1), meaning that the MLIPs are initialized alongside SANDER and the memory is retained until the MD simulation finishes. This approach avoids frequent memory allocation by storing the ML model in system cache for rapid access. We implemented the MLIP model in C++ to enable both inference and auto-gradient, which are essential for atomic force calculations. To integrate SANDER and MLIPs, we developed a Fortran/C++ interface, allowing MLIPs to communicate with SANDER directly through memory addresses, thereby reducing communication latency. Scheme 1: The implementation details of ML/MM module in SANDER MD engine in Amber. This module is initialized alongside SANDER. Once the MD loop begins, the ML model is invoked through an intrinsic Fortran/C++ interface, facilitating direct communication between the ML algorithms and the core MD routines. The outputs generated by the ML model are then meticulously processed, then they are incorporated into the SANDER mainstream.