Fig. 3: Workflow for training property models and machine learning interatomic potentials in MatGL. | npj Computational Materials

Fig. 3: Workflow for training property models and machine learning interatomic potentials in MatGL.

From: Materials Graph Library (MatGL), an open-source graph deep learning library for materials science and chemistry

Fig. 3

The initial raw data includes a list of Pymatgen Structure/Molecule objects, optional global state attributes and labels such as structure-wise and PES properties. These inputs are used to preprocess training, validation and optional test sets containing a tuple of DGL graphs, labels, optional line graphs and state attributes using MGLDataset. These datasets are then fed into MGLDataLoader to create the batched inputs including graphs, state attributes and labels for training and validation. The GNN architecture is initialized with chosen hyperparameters and passed as inputs to PL training modules with training and validation data loaders.

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