name: tensorflow-physics-ml description: TensorFlow machine learning skill specialized for physics applications including neural network potentials and surrogate models allowed-tools:
- Bash
- Read
- Write
- Edit
- Glob
- Grep metadata: specialization: physics domain: science category: data-analysis phase: 6
TensorFlow Physics ML
Purpose
Provides expert guidance on TensorFlow for physics applications, including physics-informed neural networks and neural network potentials.
Capabilities
- Physics-informed neural networks (PINNs)
- Neural network potentials (NNP)
- Normalizing flows for density estimation
- Graph neural networks for molecular systems
- Automatic differentiation for physics
- TensorBoard experiment tracking
Usage Guidelines
- Architecture Design: Build appropriate neural network architectures
- PINNs: Incorporate physical constraints in loss functions
- Potentials: Train neural network interatomic potentials
- GNNs: Use graph networks for molecular systems
- Training: Monitor and optimize training with TensorBoard
Tools/Libraries
- TensorFlow
- DeepMD-kit
- SchNet