a5c-ai/tensorflow-physics-ml
library/specializations/domains/science/physics/skills/tensorflow-physics-ml/SKILL.md
TensorFlow machine learning skill specialized for physics applications including neural network potentials and surrogate models
$ install --global
skillsauthnpx skillsauth add a5c-ai/babysitter tensorflow-physics-mlInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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SKILL.md
- name:
- tensorflow-physics-ml
- description:
- TensorFlow machine learning skill specialized for physics applications including neural network potentials and surrogate models
- 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
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