The biggest advantage of the differentiable implementation of molecular force field is that now we can employ gradient descent algorithms to optimize force fields automatically in high throughput. Due to the flexibility of JAX, we can define the object function as a combination of various properties, thus optimizing them simultaneously. DMFF aims to build a variety of differentiable property estimators, so they can be combined by the users to define highly customizable object functions. Then the object functions can be optimized using the state-of-the-art optimizers implemented in external packages such as optax or jaxopt. The most trivial estimators that come naturally with the DMFF force field calculators are energies and forces estimators. Both energies and forces are the most important fitting targets in the bottom-up force field development. DMFF offers a systematic tool to conduct energy and force fittings, which is advantageous in two ways: 1. It offers a clean API interfacing with the external optimization programs, so the most advanced optimizers developed in the ML community can be directly employed with minimal coding efforts; and 2. It is now convenient to combine energies and forces with other targets, even realizing a mixed bottom-up and top-down strategy. Overall speaking, as we will show later, DMFF really makes it simple to optimize a large number of nonlinear parameters with respect to ab initio energy and force targets.