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AI GNN Perovskite Screening
This tool is a high-efficiency AI GNN Perovskite Screening assistant designed for material scientists. It supports Low-Temperature CLAS SrxA1-xFeyB1-yO3 High-Throughput discovery tasks. By leveraging Graph Neural Networks to analyze crystal structures and oxygen vacancy formation energies, it predicts the most promising perovskite compositions for Chemical Looping Air Separation, significantly accelerating your material genome engineering research.
GNN Screening Guide
Input Features
Provide accurate chemical formulas and specific descriptors like tolerance factor, electronegativity difference, or target operating temperature.
Model Output
The AI predicts oxygen vacancy formation energy, mixed ionic-electronic conductivity, and phase stability for CLAS applications.
FAQ
What is CLAS?
Chemical Looping Air Separation is a low-energy technology for producing high-purity oxygen using metal oxides as oxygen carriers.
Why GNN?
Graph Neural Networks capture the topological structure of crystals, offering superior prediction accuracy for perovskite properties compared to traditional ML.