What problem does it solve?
Researchers often generate large sets of hypothetical crystal structures that need rapid validation, property enrichment, and prioritization before expensive calculations or synthesis. This Skill automates that workflow, turning raw candidate lists into ready‑to‑use, ranked datasets.
Core Features & Use Cases
- Structure validation & analysis: checks geometry, composition, stability, and removes duplicates.
- Hierarchical property retrieval: pulls data from Materials Project, falls back to an ASE cache, then to ML predictions, caching results automatically.
- Criteria‑based filtering: applies hard constraints such as formation energy thresholds or band‑gap windows.
- Multi‑objective ranking: uses Pareto or weighted‑sum methods to order candidates for downstream experiments.
- Use case example: screening thousands of battery cathode candidates to identify the most promising compositions for DFT refinement and experimental synthesis.
Quick Start
Run the candidate-screener skill to produce a ranked list of property‑enriched structures from your generated candidates.
