Here, we note that all the tasks mentioned above can be formulated as differentiating the model with respect to either force field parameters or system geometry. Such task can be easily performed using the automatic differentiation technique. Being the basis of all modern ML platforms (e.g., tensorflow, torch, JAX, etc.), automatic differentiation allows the developers to focus on the computing process itself, while leaving the differentiation to computers. The differentiation of macroscopic properties with respect to force field parameters, combined with modern gradient descent algorithms, enables automatic optimization workflows for force field development. Meanwhile, the automatic differentiation capability naturally eases the implementation of forces and virial tensors, leading to fast MD validation. Based on different platforms, researchers have built several end-to-end differentiable MD engine, including: TorchMD, JAX-MD, and SPONGE. However, as discussed in a previous study, for almost all thermodynamic properties, end-to-end differentiation through the entire MD trajectory is not necessary. On the contrary, the deep computation graph of MD leads to extra issues such as gradient vanishing and exploding, as well as huge memory cost in backward propagation. On the other hand, a comprehensive support to more force field function forms and more types of object functions is a more urgent need. For most molecular force field developers, functions such as topological scaling (e.g., the 1-3 scaling), flexible atom typification, long-range Ewald, and polarization, etc. are critical, but are still missing in the aforementioned platforms. Currently, the only differentiable framework dedicated to force field development is JAX-ReaxFF, which is however specific to ReaxFF. So far, there is still no differentiable platform designed for molecular force field development, which is the object of this work.