chemprop
by chempropMessage Passing Neural Networks for Molecule Property Prediction
In simple words: awesome-materials-informatics is a free book and learning collection — you read it, you do not install it, for learning new skills and knowledge at your own pace. It is completely free and open source — its code is public on GitHub, where 534 people have bookmarked it as worth using.
💡 A real-life example: Scientists use tools like this to run experiments on a computer that would be too costly or dangerous in a real lab.
RepoEasy wrote this simple explanation from the project's public data — its category, topics and description — so a beginner can understand it before downloading anything. For exact technical details, the project's own README (linked below) is the final word.
Curated list of known efforts in materials informatics, i.e. in modern materials science
This is the project's own description from GitHub — we show it as-is rather than inventing details.
This project is listed under Chemistry in Science & Engineering, a category made for students, researchers, engineers and curious minds.
Do I need coding knowledge? Basic Technical Setup. (Estimate based on project type: Learning/resource-style project or tagged for learners on GitHub. Always confirm from the repo's README.)
No license was detected on GitHub for this repository. The code is publicly visible, but you should not assume it is free to use commercially — contact the owner or check the repository for license terms first.
Activity level: Maintained · Last code update: 24 Mar 2026 · Created: 15 Feb 2018 · Open issues: 9
Official links: Releases ↗ · Documentation (README) ↗
We never publish installation steps we have not verified — the README above is the source of truth.
⭐ 534 stars · 🍴 122 forks · 🐛 9 open issues · 💻 Language: — · 📄 License: None detected · Default branch: master
Message Passing Neural Networks for Molecule Property Prediction
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