synthesis-planner

Generate inorganic synthesis routes using literature, ML predictions, and template heuristics.

7|1|Updated Mar 13, 2026

Search Tags:#synthesis #template generation #literature search #materials #route planning #ml prediction

Ready to run

npx skills add https://github.com/hkqai/MatClaw --skill synthesis-planner

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: synthesis-planner
Source: https://github.com/hkqai/MatClaw/tree/main/skills/synthesis-planner
Command: npx skills add https://github.com/hkqai/MatClaw --skill synthesis-planner

SYSTEM DOCUMENTATION & REQUIREMENTS

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.

Frequently Asked Questions about synthesis-planner

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I generate a synthesis route for an inorganic material using literature data?▼

To generate a synthesis route for an inorganic material, the system first queries the Materials Project database for experimentally proven recipes. If literature data is available, it returns standardized, high-confidence synthesis pathways for your target compound.

What happens when there are no experimentally proven recipes for a target material?▼

When no experimentally proven recipes exist for a target material, the system predicts solid-state precursors and synthesis temperatures using trained neural-network ML models. If ML fails, it generates heuristic template routes with mandatory safety warnings.

Can I use this synthesis planner for hydrothermal and sol-gel methods?▼

Yes, you can use this synthesis planner for hydrothermal and sol-gel methods. It applies to inorganic synthesis scenarios including solid-state, hydrothermal, and sol-gel techniques by systematically checking literature, ML predictions, and template heuristics.

How are low-confidence synthesis route predictions handled?▼

Low-confidence synthesis route predictions are handled by enforcing a strict decision hierarchy and adding confidence scores. The system supplies mandatory safety warnings for low-confidence outputs and clearly flags template fallback routes for expert review.

Do I need the Materials Project API to plan solid-state synthesis routes?▼

Yes, you need the Materials Project API to plan solid-state synthesis routes. Access to the Materials Project database is required to search for experimentally proven literature recipes and to derive precursors for template fallback routes.