Open MatSci ML Toolkit is a framework for prototyping and scaling out deep learning models for materials discovery supporting widely used materials science datasets, and built on top of PyTorch Lightning, the Deep Graph Library, and PyTorch Geometric.
This PR adds a Calculator implementation that specializes to matsciml's pipeline/abstractions.
Currently it is only implemented for energy and force prediction, but given that ase also supports other properties like stress, multipole moments, etc. there is some flexibility in using ScalarRegressionTask for things like that.
Add matsciml.interfaces.ase.MatSciMLCalculator class that adapts Atoms representations to structures expected by matsciml models
Add two minimum working examples for using the new interface: one loading from an inexistent pretrained checkpoint, and another showing training on LiPS and using ase after a quick training run on the devset
Adds a Github action to run pytests in the interfaces submodule
This PR adds a
Calculatorimplementation that specializes tomatsciml's pipeline/abstractions.Currently it is only implemented for energy and force prediction, but given that
asealso supports other properties like stress, multipole moments, etc. there is some flexibility in usingScalarRegressionTaskfor things like that.matsciml.interfaces.ase.MatSciMLCalculatorclass that adaptsAtomsrepresentations to structures expected bymatscimlmodelsaseafter a quick training run on the devsetpytests in theinterfacessubmoduleaseroutines