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MatGL

MatGL is a materials graph learning library for developing and using property predictors and machine learning potentials.

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Overview

The library provides several atomistic graph architectures and workflows for pretrained models. This entry covers the toolkit rather than duplicating individual M3GNet, MEGNet or CHGNet entries.

Limitations

Version 3 removes DGL and corrects message passing in some architectures. Use matching new checkpoints rather than legacy weights.

Key Features

  • Materials graph model development
  • Pretrained interatomic potential workflows

Use Cases

  • Study materials property prediction
  • Evaluate learned potentials for structural calculations

How to Use

Follow the official installation guide, choose a model and compatible checkpoint, and prepare structures using the documented examples. Record versions and output units.

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