Optimizing data difference between dataset building and AtomicData.from_points/from_ase/...

[floatingCatty]

Background

The user often uses the latter api to perform calculations on jupyter notebook, however, it does not have some vital properties as the one gets from AtomicData built from dataset. Including:

1. some initialized node/edge/overlap features
2. SOC related features
3. atom/bond type features that are mapped from idp.

This is very hurtful since the user tends not to know where the AtomicData are getting from,causing troubles for our daily usage.

Describe the solution you'd like

Figure out a way to optimizing this, either by unify the process of getting atomic data class, or making the dependence on the parameters such as SOC and bond/atom type implicit.

Additional Context

No response