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
2. Collect and curate dataset
3. Generate features (composition, structure)
4. Handle missing values

1. Model Development
2. Select appropriate architecture
3. Train with cross-validation
4. Evaluate on held-out test

1. Application
2. Screen candidate materials
3. Prioritize experiments
4. Validate predictions

Process Integration

• Machine Learning Materials Discovery Pipeline
• Structure-Property Correlation Analysis

Input Schema

{
  "dataset_file": "string",
  "target_property": "string",
  "model_type": "random_forest|gnn|cgcnn|megnet",
  "features": "composition|structure|both",
  "task": "train|predict|screen"
}

Output Schema

{
  "model_performance": {
    "mae": "number",
    "rmse": "number",
    "r2": "number"
  },
  "predictions": [{
    "material": "string",
    "predicted_value": "number",
    "uncertainty": "number"
  }],
  "top_candidates": [{
    "material": "string",
    "predicted_property": "number",
    "rank": "number"
  }]
}