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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.