Welcome to AGAT’s documentation!

Github Homepage

Note

Navigate to v1.0.0 for the documentation and source codes to reproduce results and support conclusions of Design High-Entropy Electrocatalyst via Interpretable Deep Graph Attention Learning.

AGAT:

• Introduction
  • Atomic Graph ATtention (AGAT)
  • API documentation
  • Source code
• Installation
  • 1. Install with conda environment configuration file
  • 2. Install manually with conda
  • 3. Install with Conda environment on Linux.
• Quick start
  • Prepare VASP calculations
  • Collect paths of VASP calculations
  • Build database
  • Train AGAT model
  • Application (geometry optimization)
  • Application (high-throughput prediction)
  • Tips:

AGAT Tutorial:

• Brief
  • Take a look at the default parameters
  • Easy usage
  • Customized usage
• More tutorials
  • VASP calculations
  • Build graph
  • Build database
  • Train AGAT model
  • Predict_structural_file
  • Application (structural optimization)
  • Application (high-throughput prediction)

中文手册:

• 中文手册
  • 中文手册持续更新中，您可在GitHub提问或点此添加开发者微信讨论交流：

AGAT API:

• API_Docs
  • default_parameters
  • lib
  • data
  • model
  • app
  • bin

OTHER:

• Contribution
• Reference
• Frequently asked questions (FAQ)
• Change log
  • v9.1.0
  • v9.0.1
  • v9.0.0
  • v8.0.3
  • v8.0.3
  • v8.0.0
  • v7.14.0
  • v7.13.4
  • v7.13.3
  • v7.13.2
  • v7.13.1
  • v7.13
  • v7.12.2
  • v7.12.1
  • v7.12
  • v1.0.0

Indices and tables

• Index

• Module Index

• Search Page