Replaces classical empirical force fields with neural network potentials for atomic simulations. Combines TensorFlow with chemical physics to predict molecular energies and forces with DFT-level accuracy.
Tech Stack
PythonChemistry & Materials
Why TensorMol?
• High computational efficiency
• Seamless TensorFlow integration
• Supports diverse atomic species
Limitations
• High initial training cost
• Requires deep learning expertise
• Complex model architecture
2/9/2026
Last Update
72
Forks
18
Issues
GPL-3.0
License
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