Chemellia: An Ecosystem for Atomistic Scientific Machine Learning
This provides a domain-specific tool for researchers in computational chemistry and materials science, but it is incremental as it builds on existing interfaces and paradigms.
The authors introduced Chemellia, an open-source Julia framework for atomistic machine learning that emphasizes code reuse and interoperability through multiple dispatch, and demonstrated its application with crystal graph convolutional neural networks for material property prediction.
Chemellia is an open-source framework for atomistic machine learning in the Julia programming language. The framework takes advantage of Julia's high speed as well as the ability to share and reuse code and interfaces through the paradigm of multiple dispatch. Chemellia is designed to make use of existing interfaces and avoid ``reinventing the wheel'' wherever possible. A key aspect of the Chemellia ecosystem is the ChemistryFeaturization interface for defining and encoding features -- it is designed to maximize interoperability between featurization schemes and elements thereof, to maintain provenance of encoded features, and to ensure easy decodability and reconfigurability to enable feature engineering experiments. This embodies the overall design principles of the Chemellia ecosystem: separation of concerns, interoperability, and transparency. We illustrate these principles by discussing the implementation of crystal graph convolutional neural networks for material property prediction.