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* temporarily pin emmet core
* change band structure test to reflect new emmet model
* tweaks for emmet-compat
* bug free emmet bump?
* precommit
* bump minimum emmet core
* bump minimum emmet core version
* add cached entry property to ase/forcefield docs
* pip emmet core
* drop 3.10 due to upstream drop
* un double gzip some potcar specs
* prevent uploading tutorial output
* bump matgl in strict + test data updates
* cleanup tests
* try to pin numpy
* try to pin numpy
* try debugging why notebook is not working in ci but is locally
* that is why
* move entry to ase / mlff schema fields instead of property
* restore pymatgen objects using default toggle to pymatgen
* compat with emmet toggle
* new emmet min pin for new ver
* update docs
* add toggle for forcefield and ase jobs
* precommit
* micromamba issues?
* micromamba issues?
The `emmet-core` package is used to define data schemas for parsing outputs of workflows.
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It is also used in building the Materials Project data, therefore its use in `atomate2` is to broadly ensure compatibility with the Materials Project's data structures.
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`emmet-core` allows you to use either `pymatgen` or `emmet-core`-defined models for larger data objects, such as charge densities (`CHGCAR`, `AECCAR*`), or trajectories (relaxation, MD, etc.).
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The `pymatgen` objects have long been the default in workflows, and are structured to be output as JSON files.
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The `emmet-core` objects have been designed with both JSON and Apache parquet as storage formats.
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If you will be storing data in the cloud, or would like to use these newer data models which may use less storage, you can either add a line to your `atomate2.yaml` file:
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```yaml
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VASP_USE_EMMET_MODELS: true
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```
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or use an environment variable in your `.bashrc`:
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```console
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export ATOMATE2_VASP_USE_EMMET_MODEL=true
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```
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Note that there is an equivalent `emmet-core` setting, which can be set by the environment variable `EMMET_USE_EMMET_MODELS`.
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For ASE and machine learning forcefield jobs, you can use the `ASE_FORCEFIELD_USE_EMMET_MODELS` flag in `atomate2.yaml` to toggle the same functionality.
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The default in `atomate2` is to use `pymatgen` models.
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