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materialsvirtuallab / matgl

Graph deep learning library for materials
BSD 3-Clause "New" or "Revised" License
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Fix jupyter notebook for M3GNet property model and MLIP training #247

Closed kenko911 closed 3 months ago

kenko911 commented 3 months ago

Summary

Including include_line_graph=True for M3GNet training in the jupyter notebook

Checklist

Tip: Install pre-commit hooks to auto-check types and linting before every commit:

pip install -U pre-commit
pre-commit install

Summary by CodeRabbit

coderabbitai[bot] commented 3 months ago

Walkthrough

The updates focus on transitioning from the MEGNet model to the M3GNet model within a PyTorch Lightning framework for materials science applications. Key modifications include renaming the model across various sections, introducing an include_line_graph parameter to enhance dataset and model configuration, and refining data handling with functools.partial for improved flexibility. This transition marks a significant update aimed at leveraging M3GNet's capabilities for predicting formation energy and potential.

Changes

File Path Changes
examples/.../Training a M3GNet Formation Energy Model with PyTorch Lightning.ipynb - Renamed "MEGNet" to "M3GNet"
- Added include_line_graph parameter
examples/.../Training a M3GNet Potential with PyTorch Lightning.ipynb - Added functools.partial import
- Included include_line_graph=True in dataset and model configurations

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In the realm of atoms and bonds,
Where data like a river flows,
M3GNet rises, magic wands,
Transforming zeros into pros.

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