🔬There Is No AlphaFold for Materials — AI for Materials Discovery with Heather Kulik
Mar 24, 2026 · 35:15
Heather Kulik, MIT professor, demonstrates that AI can discover surprising new materials—such as a polymer made four times tougher via an unexpected quantum effect—but warns current models still fail basic chemistry tasks like generating a 22-atom ligand. She describes active learning with seven objectives to accelerate discovery of metal-organic frameworks for direct CO₂ capture, achieving hundred- to thousand-fold speedups per dimension. Kulik criticizes machine-learned potentials that 'look really good' but often produce nonsensical results, and notes that no large-scale experimental benchmark like CASP exists for materials. She calls for shared high-throughput cloud labs and standardized data reporting so published results are machine-learning-ready from day one. Her group's open-source tool MolSimplify (and MOFSimplify) generates transition-metal complexes and screens MOFs, and she invites feedback from users.
