BMAD-METHOD/.claude/rules/pytorch-scikit-learn-cursorrules-prompt-file/chemistry-ml---pytorch-models.mdc

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description: Guidelines for deep learning model development with PyTorch in chemistry applications, including network architecture, batch processing, and optimization techniques.
globs: models/pytorch/**/*.py
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- Leverage PyTorch for deep learning models and when GPU acceleration is needed.
- Design neural network architectures suitable for chemical data (e.g., graph neural networks for molecular property prediction).
- Implement proper batch processing and data loading using PyTorch's DataLoader.
- Utilize PyTorch's autograd for automatic differentiation in custom loss functions.
- Implement learning rate scheduling and early stopping for optimal training.
- Use GPU acceleration when available, especially for PyTorch models.