About AI4Science Hub
A curated collection of resources for applying AI and machine learning to scientific research.
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 learning 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
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.
How to Contribute
- Add a resource — Open a pull request with the new material
- Fix an error — Issues and PRs welcome
- Suggest improvements — Open a discussion
Contribution Guidelines
- Resources should be high quality and actively maintained
- Prefer open-access materials
- Include a brief description of what makes the resource valuable
- Check that links work
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
Content is licensed under CC0 1.0 — free to use, share, and adapt.
The site code is open source under MIT license.