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"project_name": "MatDeepLearn",
"project_description": "A platform for testing and using graph neural networks (GNNs) for materials chemistry applications",
"repository": "https://github.com/Fung-Lab/MatDeepLearn",
"mcp_tools": [
{
"name": "check_environment",
"description": "Check if MatDeepLearn environment is properly configured and GPU is available"
},
{
"name": "list_available_models",
"description": "List all available GNN models in MatDeepLearn"
},
{
"name": "get_model_config",
"description": "Get the default configuration for a specific model"
},
{
"name": "process_structure_data",
"description": "Process atomic structure data into graph format for GNN training"
},
{
"name": "train_model",
"description": "Train a GNN model on processed structure data"
},
{
"name": "predict_properties",
"description": "Use a trained model to predict properties of new structures"
},
{
"name": "cross_validation",
"description": "Perform k-fold cross validation on a dataset"
},
{
"name": "analyze_structure",
"description": "Analyze the structure of atomic data and convert to graph representation info"
},
{
"name": "compare_models",
"description": "Compare performance of different GNN models on a dataset"
},
{
"name": "get_dataset_info",
"description": "Get information about a dataset directory"
}
],
"supported_models": [
"CGCNN_demo",
"MPNN_demo",
"SchNet_demo",
"MEGNet_demo",
"GCN_demo",
"SOAP_demo",
"SM_demo"
],
"dependencies": [
"torch",
"torch-geometric",
"ase",
"pymatgen",
"fastmcp",
"numpy",
"scipy",
"scikit-learn"
],
"python_version": ">=3.8",
"created_at": "2025-12-03",
"transport_modes": ["stdio", "http"]
}
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