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

### 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_

Compare the dataset-building path with the AtomicData.from_points and AtomicData.from_ase entry points, focusing on initialized node, edge, overlap, SOC, and atom/bond type features. First document which properties differ and which parameters currently control them; done means the construction paths provide consistent AtomicData properties or clearly expose their required differences.

Written by the indexing model from the issue text.