---
name: tensorflow-physics-ml-2a18b6a69ce2
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
graph:
  domains: [domain:physics]
  skillAreas: [skill-area:machine-learning-frameworks, skill-area:physics-simulation, skill-area:statistical-analysis]
  workflows: [workflow:experiment-design, workflow:peer-review-cycle]
  roles: [role:research-scientist, role:ml-engineer]
---

# 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


---
Source attribution: a5c-ai
Original project: https://github.com/a5c-ai/babysitter
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

MIT License

Copyright (c) 2026 Gabriel Moreira

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.



# Mega AI Hub resource metadata

Creator / publisher: a5c-ai
Original project: https://github.com/a5c-ai/babysitter
Attribution basis: Upstream repository explicitly recorded in package mirror path

Fields of work: Data & Analytics, Development & Engineering, Education & Research, Product Management
Roles: Data Scientist, ML / AI Engineer, Product Manager, Researcher, Technical Product Manager
Specialties: API, Backend
Tools: Bash, Deepmd Kit, Schnet, Tensorflow
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: f62fb4c35fa6d48469872522e8a34ccbfa622cf0316fcd3e47a6388262ab50ec