Replaces standard graph neural networks with SE(3)-equivariant transformers for molecular property prediction. Leverages equivariant graph attention to maintain geometric symmetry in 3D atomistic graphs.
Tech Stack
PythonAI, ML & Data
Why equiformer?
• Rotationally invariant
• High data efficiency
• PyTorch native
Limitations
• High GPU memory usage
• Complex math foundation
• Slow training times
3/2/2026
Last Update
52
Forks
11
Issues
MIT
License
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