AI for Science
AlphaFold, protein language models, materials discovery, and the pitfalls of ML-for-science.
12concepts
137flashcards
99minutes of reading
- 01 Machine Learning for Materials Discovery How graph neural networks screen millions of candidate crystals for stability and how machine-learning interatomic potentials approximate DFT cheaply enough to simulate them, plus why an in-silico "stable" material is not yet a real one.
- 02 Pitfalls in ML-for-Science The failure modes, chiefly data leakage, that make machine-learning results in scientific papers look stronger than they replicate, and the reporting standards proposed to catch them.
- 03 Protein Language Models How masked-language-model pretraining over amino-acid sequences produces structure and function signal, and how ESMFold trades some accuracy for dropping the MSA search that AlphaFold2 depends on.
- 04 Self-Driving Labs and Autonomous Experimentation The A-Lab synthesised 41 of 58 target compounds in 17 days with no human intervention, and the dispute that followed — ending in a 2026 author correction — is the clearest available lesson in what an autonomous laboratory actually automates.