synthesis-planner

What problem does it solve?

Researchers and AI agents need reliable synthesis pathways for inorganic materials, but existing tools often ignore verified literature or provide unvalidated heuristics, leading to wasted experiments and unsafe conditions.

Core Features & Use Cases

• Literature‑First Search: Automatically queries the Materials Project database for experimentally proven recipes, returning standardized routes when available.
• ML‑Based Prediction: When no literature exists, predicts solid‑state precursors and synthesis temperatures using trained neural‑network models.
• Template Fallback: Generates heuristic routes with MP‑derived precursors if both literature and ML options fail, and clearly flags them for expert review.
• Safety & Validation: Enforces strict decision hierarchy, adds confidence scores, and supplies mandatory warnings for low‑confidence outputs.
• Use Case Example: A materials scientist asks for a synthesis route for LiCoO₂; the skill returns high‑confidence literature routes, otherwise falls back to ML or template routes with appropriate cautions.

Quick Start

Ask the synthesis‑planner skill to generate a synthesis route for LiCoO₂ using default settings.