ml-materials-predictor

# ML Materials Predictor ## Purpose The ML Materials Predictor skill provides machine learning capabilities for accelerated nanomaterial discovery and property prediction, enabling data-driven approaches to materials design and optimization. ## Capabilities - Feature engineering for materials - Property prediction models (GNN, transformers) - Active learning for experiment design - High-throughput virtual screening - Synthesis success prediction - Transfer learning for small datasets ## Usage Guidelines ### ML Materials Workflow 1. **Data Preparation** - Collect and curate dataset - Generate features (composition, structure) - Handle missing values 2. **Model Development** - Select appropriate architecture - Train with cross-validation - Evaluate on held-out test 3. **Application** - Screen candidate materials - Prioritize experiments - Validate predictions ## Process Integration - Machine Learning Materials Discovery Pipeline - Structure-Property Correlation Analysis ## Input Schema ```json { "dataset_file": "string", "target_property": "string", "model_type": "random_forest|gnn|cgcnn|megnet", "features": "composition|structure|both", "task": "train|predict|screen" } ``` ## Output Schema ```json { "model_performance": { "mae": "number", "rmse": "number", "r2": "number" }, "predictions": [{ "material": "string", "predicted_value": "number", "uncertainty": "number" }], "top_candidates": [{ ...