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

{
  "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"
  }]
}