Thrust 3: Phenomenlogical Initialization for Magnetic Structures

Objective

Develop an AI-driven expert system to predict and optimize spin configurations in transition metal oxides.

System Features

• Knowledge Base:
  • Database of known spin configurations.
• AI Algorithms:
  • Reinforcement learning to optimize configurations.
  • Graph neural networks for spin-state prediction.
• Integration:
  • Link with density functional theory (DFT) workflows.

Applications

• Designing materials for quantum computing.
• Enhancing catalytic properties in oxides.

How to Use

1. Input system properties (e.g., lattice type, transition metal type).
2. Run the AI-driven optimization.
3. Analyze results with visualization tools.

Check the Spin Configuration Expert System Guide for details.

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