# Tensorflow Physics Ml

> TensorFlow machine learning skill specialized for physics applications including neural network potentials and surrogate models

- Skill: `a5c-ai/tensorflow-physics-ml` (Agent Skill)
- Install (CLI): `npx skillmds@latest add a5c-ai/tensorflow-physics-ml`
- Raw SKILL.md: https://api.skillmd.com/api/skills/a5c-ai/tensorflow-physics-ml/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: a5c-ai (https://skillmd.com/u/a5c-ai)
- Updated: 2026-09-09
- Page: https://skillmd.com/skills/a5c-ai/tensorflow-physics-ml

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# 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

1. **Architecture Design**: Build appropriate neural network architectures
2. **PINNs**: Incorporate physical constraints in loss functions
3. **Potentials**: Train neural network interatomic potentials
4. **GNNs**: Use graph networks for molecular systems
5. **Training**: Monitor and optimize training with TensorBoard

## Tools/Libraries

- TensorFlow
- DeepMD-kit
- SchNet