Generative AI for Science

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Master generative AI for scientific discovery with this hands-on guide. Learn Transformers, Diffusion Models, VAEs, GNNs & PINNs through 50+ Google Colab notebooks. Apply AI to drug discovery, protein structure prediction, climate modeling & more. 500+ pages of practical codeβ€”no setup required. By Dr. J. Paul Liu.

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Generative AI for Science15 chapters

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  1. From the Author

  2. Chapter 1 β€” Generative AI: A New Frontier for Scientific Discovery

  3. Chapter 2: Generative AI Fundamentals

  4. Chapter 3: Scientific Data & Workflows

  5. Chapter 4: Text, Code & Knowledge Generation for Scientists

  6. Chapter 5: Data-to-Data Models

  7. Chapter 6: Physics-Informed AI and Simulation

  8. Chapter 7: Domain Applications in Chemistry, Biology, Physics and Geoscience

  9. Chapter 8: Fine-Tuning & Domain Adaptation

  10. Chapter 9: Multimodal Generative AI for Sciences

  11. Chapter 10: Evaluation, Validation & Benchmarking

  12. Chapter 11: Ethics & Responsible AI for Science

  13. Chapter 12: Deployment & MLOps for Scientific Applications

  14. Chapter 13: Future Directions & Conclusion

  15. Acknowledgements