[Feature Request] Support parameter optimization for dynamic properties based on trajectory

### Summary

Expand the function of parameter optimization in DMFF for dynamic properties, such as diffusion coefficient, viscosity, etc. This feature requests,

1. An efficient algorithm for evaluating the gradient.
2. A robust optimization strategy.
3. A general interface for computing all kinds of dynamic properties.

### Motivation

In v0.2.0, DMFF already support parameter optimization for some thermodynamic properties, such as free energy, RDF, etc. However, for more complicated dynamic properties, DMFF can't get the gradient from the trajectory. Expanding the function of parameter optimization for dynamic properties helps DMFF to be a more powerful engine to accelerate forcefield development in real applications.

### Suggested Solutions

Many dynamic properties can be generally evaluated using time-correlation function, which utilizes the information of every state in the trajectory. If we simply use `jax.grad` on the whole trajectory, the memory may be a huge problem. Inspired by Neural ODE, we can evaluate the gradient in backpropagation process , which utilizes the time reversibility of NVE trajectory. As we get the gradient, we can use modern AI optimization algorithm to fit the dynamic properties.

Any suggestions and comments on this solution are welcomed !

### Further Information, Files, and Links

_No response_