aimat-lab/gcnn_keras
Graph convolutions in Keras with TensorFlow, PyTorch or Jax.
This is a framework for materials scientists and chemists to build and apply graph neural networks for molecules and crystalline materials. It takes in structured data representing chemical graphs (nodes as atoms, edges as bonds) with their properties. It outputs predictions or insights about material characteristics, enabling researchers to leverage advanced machine learning without deep low-level coding.
118 stars. No commits in the last 6 months.
Use this if you are a researcher in chemistry or materials science needing to apply graph neural networks to predict properties of molecules or crystals and prefer using Keras.
Not ideal if your primary domain is not molecules or materials, or if you need to work with highly dynamic graph structures that don't fit into batched or disjoint tensor representations.
Stars
118
Forks
30
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Jan 08, 2025
Commits (30d)
0
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