Schnetpack

For this release, we refactored configurations and script names such that the training and generation can be invoked and configured more conveniently. The README has been updated to reflect these changes.

We provide a new script called `check_validity.py` that allows to assess the validty, uniqueness, and novelty of generated molecules in a standardized way. It builds on the implementation of [xyz2mol](https://github.com/jensengroup/xyz2mol) available in [RDKit](https://www.rdkit.org/docs/source/rdkit.Chem.rdDetermineBonds.html). See the [README](https://github.com/atomistic-machine-learning/schnetpack-gschnet/blob/main/README.md#filtering-molecules) for instructions.

The model checkpoint now stores additional information on training settings, e.g. the distance unit used in the reference structures.
Furthermore, additional information about the settings chosen for molecule generation are stored in the data bases of generated molecules.

The changes might lead to errors when loading models trained with previous releases, so use previous versions to generate molecules with older models.

This marks the first official release of `schnetpack-gschnet` as an extension to `schnetpack>=2.0.3`.
Split files generated using the pre-release 0.0.1 are not compatible with v1.0.0.
Other than that, models trained with the pre-release are compatible.

Initial release of the code base that is compatible with `schnetpack==2.0.1` and `pytorch-lightning<2.0`.
Fixed versions of required packages in [setup.py](https://github.com/atomistic-machine-learning/schnetpack-gschnet/blob/69c23469591969f2c684b3a69a423b5546b14daf/setup.py) to guarantee that a functioning environment can be easily set up by following the installation instructions.
The next release will have relaxed version constraints and it will work with the most up-to-date dependencies (i.e. `schnetpack`, `pytorch-lightning`, `pytorch` etc.).