by Linus-He · Agent Tool · ★ 57
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EcoMOF-AI EcoMOF-AI is a research prototype for transparent MOF screening, adsorption-performance interpretation, sustainability comparison, and catalysis-oriented evidence structuring. It is designed for exploratory scientific workflows where every score, label, and visualization must keep its data boundary visible. Live site: https://linus-he.github.io/ecomof-ai/ Project Motivation MOF screening tools often compress structural descriptors, adsorption labels, sustainability proxies, and literature evidence into a single rank without showing where the data came from or where it is missing. EcoMOF-AI takes the opposite approach: it keeps candidate prioritization useful while exposing data mode, curation status, descriptor provenance, and validation gaps. The current application is not a publication-grade predictor. It is a browser-based decision-support prototype for comparing candidate records, organizing evidence, and identifying what must be measured or curated next. Key Features Data Provenance: field-level source popovers and dataset notes identify whether values are curated, pending, inferred, demo-only, or source-record placeholders.
| Stars | 57 |
| Forks | 0 |
| Language | JavaScript |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 62.7212971910706/100 |
| Last Updated | 2026-05-14 |
| Created | 2026-03-29 |
| Platforms | node |
| Est. Tokens | ~209k |
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ecomof-ai is EcoMOF-AI | MOF Decision Support Prototype. It is categorized as a Agent Tool with 57 GitHub stars.
ecomof-ai is primarily written in JavaScript. It covers topics such as ai, ai-tools, applied-chemistry.
You can find installation instructions and usage details in the ecomof-ai GitHub repository at github.com/Linus-He/ecomof-ai. The project has 57 stars and 0 forks, indicating an active community.
ecomof-ai is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to ecomof-ai on Agent Skills Hub include Promptimizer, cai, toolsdk-mcp-registry. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.