Chapter 1: Introduction: Prediction, Understanding, and Deep Neural Networks
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Chapter 2: AlphaFold: Prediction without Understanding
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11. John Jumper et al., “Highly Accurate Protein Structure Prediction with AlphaFold,” Nature, 596 (2021), 583–589.10.1038/s41586-021-03819-2
12. Brian Kuhlman et al., “Design of a Novel Globular Protein Fold with Atomic-Level Accuracy,” Science, 302, no. 5649 (2003), 1364–1368.10.1126/science.1089427
13. Peter Haahr et al., “Actin Maturation Requires the ACTMAP/C19orf54 Protease,” Science, 377, no. 6614 (2022), 1533–1537.10.1126/science.abq5082
14. James P. Rooney and Sergey Ovchinnnikov, “State-of-the-Art Estimation of Protein Model Accuracy Using AlphaFold,” Physical Review Letters, 129 (2022), 238101.10.1103/PhysRevLett.129.238101
15. Google DeepMind, “AlphaFold: A Solution to a 50-Year-Old Grand Challenge in Biology,” Google DeepMind blog, November 30, 2020, https://deepmind.google/blog/alphafold-a-solution-to-a-50-year-old-grand-challenge-in-biology/; Cade Metz, “London A.I. Lab Claims Breakthrough That Could Accelerate Drug Discovery,” The New York Times, November 30, 2020; Jonathan Chadwick, “50-Year-Old ‘Protein Folding Problem’ Is Finally SOLVED by Google-Owned AI in ‘Stunning Advance’ That Could Lead to Faster Drug Discoveries,” The Daily Mail, December 1, 2020.
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Chapter 3: Physics: Learning from Networks
1. Mario Krenn et al., “Automated Search for New Quantum Experiments,” Physical Review Letters, 16 (2016), 090405.10.1103/PhysRevLett.116.090405
2. Mario Krenn, Jakob S. Kottmann, Nora Tischler, and Alán Aspuru-Guzik, “Conceptual Understanding Through Efficient Automated Design of Quantum Optical Experiments,” Physical Review X, 11 (2021), 031044.10.1103/PhysRevX.11.031044
3. Mario Krenn et al., “On Scientific Understanding with Artificial Intelligence,” Nature Reviews Physics, 4 (2022), 761–769.10.1038/s42254-022-00518-3
4. Silviu-Marian Udrescu and Max Tegmark, “AI Feynman: A Physics-Inspired Method for Symbolic Regression,” Science Advances, 6, no. 16 (2020)10.1126/sciadv.aay2631, DOI: 10.1126/sciadv.aay2631
5. Miles Cranmer et al., “Discovering Symbolic Models from Deep Learning with Inductive Biases,” 34th Conference on Neural Information Processing Systems, Vancouver, Canada, 2020, https://proceedings.neurips.cc/paper_files/paper/2020
6. Pablo Lemos et al., “Rediscovering Orbital Mechanics with Machine Learning,” Machine Learning: Science and Technology, 4, no. 4 (2023), 045002.
7. Raban Iten, Artificial Intelligence for Scientific Discoveries: Extracting Physical Concepts from Experimental Data Using Deep Learning (Cham: Springer, 2023)10.1007/978-3-031-27019-2.
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10. Philippe Gras et al., “Systematics of Quark/Gluon Tagging,” Journal of High Energy Physics, (2017), 91.10.1007/JHEP07(2017)091
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12. Patrick T. Komiske, Eric M. Metodiev, and Jesse Thaler, “Energy Flow Networks: Deep Sets for Particle Jets,” Journal of High Energy Physics (2019), 121.10.1007/JHEP01(2019)121
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Chapter 4: Neuroscience: Letting Go of the Dream
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Chapter 5: Conclusion: The Rise of AI Scientists
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2. David Adam, “The AI Co-scientist Is Here,” Nature Medicine, 32 (2026), 772.10.1038/s41591-026-04275-z
3. Matthew Schwartz, “Vibe Physics: The AI Grad Student,” Anthropic, March 23, 2026, www.anthropic.com/research/vibe-physics
4. Ted Chiang, “Catching Crumbs from the Table,” Nature, 405 (2000), 517.10.1038/35014679
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