Ecnet

Sets alvaDesc `na\r` return values to `0.0`

- update all package dependencies

- Build/installation now uses `pyproject.toml` instead of the deprecated `setup.py`
- Added GitHub workflows for PyPI publishing and unit testing
- Unit tests now use `pytest` instead of `unittest`

Update to package dependencies, notably PyTorch 1.8.0 -> 2.0.0. ECNet now requires Python 3.11+.

- Added option to shuffle training/validation subsets every epoch
- Update to docstrings/documentation
- Added a "getting started" notebook in the examples directory
- New argument format for ABC-based parameter tuning

- ECNet now leverages the PyTorch package for ML operations
- This change presented an opportunity to overhaul ECNet from the ground up, allowing us to think about _how_ the user will interact with this package. Ultimately, we wanted to make interactions easier.
- Custom data structures were weird, and didn't belong in a ML toolkit. Instead, we offer PyTorch-based data structures, adjusted to house chemical data. Users can obtain SMILES strings and property values, or a ML-ready structure ready to be passed to ECNet for training.
- All these changes require documentation, so full API documentation is available. We also have an example script, and would like to include more examples in the future.