ML Materials Predictor Skill

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

Data Preparation
- Collect and curate dataset
- Generate features (composition, structure)
- Handle missing values
Model Development
- Select appropriate architecture
- Train with cross-validation
- Evaluate on held-out test
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"
  }]
}

Agent Skills: ML Materials Predictor

Install this agent skill to your local

Skill Files