mofdscribe is a Python library for digital reticular chemistry. mofdscribe contains 40+ featurizers that have been adapted from scientific publications (or that have been not reported so far) and are accessible using the consistent and battle-proof matminer API.
mofdscribe is open source via a MIT license.
Currently, machine learning practitioners in the field of reticular chemistry are using a wide variety of tools, and different scripts, to compute features as input for machine learning studies or to perform other steps in the ML workflow. The main goal of this project is to provide a unified interface for digital reticular chemistry: from dataset up to publication.
Since many featurizers are quite domain specific, and require external dependencies, the featurizers are currently not integrated in matminer itself.
- Getting started
- Addressing data leakage in digital reticular chemistry
- Data splitters
- Datasets in mofdscribe
- Extending and contributing to mofdscribe
- API documentation
- Maintaining mofdscribe