TensorFlow Physics ML

SkillAI & models

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

Instructions available. Your AI can read the instructions. Execution depends on the setup they require.

Account requirements not reviewed. Check the skill instructions before use; ahel provides instructions and does not run this skill.

Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.

Then ask your AI: use the TensorFlow Physics ML skill

What this skill tells your AI

The instructions your AI receives, as published by a5c-ai/babysitter in library/specializations/domains/science/physics/skills/tensorflow-physics-ml/SKILL.md and read by ahel’s review.

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

Signals

GitHub stars: 2k
Forks: 112
Last commit: Sep 2026

Advanced

Item type: skill
Key: tensorflow-physics-ml
Source: github.com/a5c-ai/babysitter

github.com/a5c-ai/babysitter