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Project description
MEGNetSparse
Installation
pip install MEGNetSparse
- You must first install the torch and torch-geometric
- The notebook provided in the examples will only work with pymatgen=2023.1.30, so you may need to reinstall it.
Usage
The library provides the ability to use a function convert_to_sparse_representation and a class MEGNetTrainer
convert_to_sparse_representation(
structure,
unit_cell,
supercell_size,
skip_eos=True,
skip_was=False,
skip_state=False,
copy_unit_cell_properties=False
)
- structure : Structure - the structre to convert to sparse representation
- unit_cell : Structure - unit cell of base material
- supercell_size : List[int] - list with three integers to copy a cell along three coordinates
- skip_eos : bool - if True will not add eos to properties and will speed up computations
- skip_was: bool - if True will not add was to properties
- skip_state : bool - if True will not add global state
- copy_unit_cell_properties: bool - if True will also copy unit cell properties in case of name collisions structure properties will be overwritten
return : sparse representation of structure
MEGNetTrainer(
config,
device,
)
- config : dict - template config can be found in examples notebook
- device : str - device in torch format
MEGNetTrainer.prepare_data(
self,
train_data,
train_targets,
test_data,
test_targets,
target_name,
):
- train_data : List[Structure] - list of structures in sparse or dense representation
- train_targets : List[float32] - list of targets
- test_data : List[Structure] - list of structures in sparse or dense representation
- test_targets : List[float32] - list of targets
- target_name : str - target name
MEGNetTrainer.train_one_epoch(self)
return : mae on train data, mse on train data
MEGNetTrainer.evaluate_on_test(
self,
return_predictions=False
)
return : if return_predictions=True, mae on test data, predictions else only mae on test data
MEGNetTrainer.predict_structures(
self,
structures_list
)
- structures_list : List[Structure] - list of structures in sparse or dense representation
return : predictions for structures
MEGNetTrainer.save(self, path)
- path : str - where to store model data
MEGNetTrainer.load(self, path)
- path : str - where to load model data from
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