About AI4Science Hub
Mission
The AI4Science Hub exists to lower the barrier for researchers entering the intersection of AI and science. We curate, organize, and present the best resources so you can focus on learning, not searching.
What We Curate
- Courses — Structured learning from universities and research groups
- Tutorials — Hands-on notebooks and practical guides
- Blogs — Expert perspectives and latest developments
- Communities — Places to connect and collaborate
- Tools & Datasets — Essential software and data resources
Tools are ordered by adoption (what to reach for first); datasets are grouped by use case. Sections with many items are split into subsections to keep scanning easy.
Philosophy
- Quality over quantity — We’d rather have 10 excellent resources than 100 mediocre ones
- Practicality — Resources should be actionable, not just theoretical
- Accessibility — We prefer free, open-access materials when possible
- Currency — We actively maintain and update our listings
Contributing
This is an open resource. We welcome contributions from the community.
Content lives in the hub-content repository. To add or update a resource:
- Fork the repo, find the appropriate topic file, and add your resource following the existing format
- Open a pull request with a descriptive title (e.g. “Add AlphaFold tutorial to biology/tutorials”) and a short note on why the resource is valuable
Guidelines
- Quality over quantity — Only submit resources that are genuinely valuable
- Prefer open access — Free, openly available materials are preferred
- Check your links — Ensure all URLs work before submitting
- Include context — Add a brief description explaining what makes the resource useful
- Active maintenance — Resources should be actively maintained (no abandoned projects)
What makes a good contribution
- Courses from reputable universities or research groups
- Tutorials with working code and clear explanations
- Tools that are well-documented and widely used
- Datasets with clear licensing and documentation
- Blog posts with unique insights or practical guidance
Reporting issues
Open an issue for broken links, outdated information, or suggestions for new topics or restructuring.
Acknowledgments
This project was inspired by awesome-learning-digital-chemistry by Magdalena Lederbauer and the broader “awesome list” tradition.
Special thanks to:
- The maintainers of all listed resources
- The open source chemistry and materials communities
- Everyone who contributes to making science more accessible
License
The site code and content are open source under the MIT license.