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Technical outline
Marcus Wieder edited this page Nov 7, 2023
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We will base our work on the following packages for specific tasks.
Task | Package |
---|---|
Model definition | pytorch |
QM calculations and dataset |
QCarchive , QCSubmit
|
Experiment control, reproducibility, model shipping | Data Version Control |
units | openff-units |
hyper-parameter selection and optimization | ray |
The package has the following overall structure
modelforge
datasets/ # retrieve and prepare different datasets to enable efficient training
curation/ # curate datasets to make them usable (the pipeline before uploading to zenodo)
interface/ # interface to other molecular simulation packages
potential/ # defines all operations needed to implement a nnp and implement nnps
train/ # define hyper-parameters and their optimization and training routine
utils/ # helper functions