A generative model for inorganic materials design

I’m glad they actually tried synthesizing one of the materials their model predicted. Looks like they succeeded in synthesizing only 1 out of 4 of the materials for which they tried. The 20% accurate property claim appears to be for bulk modulus. I’m still seeing little value for this technology for designing electronic properties, mainly because density functional theory which provides the training data is not reliable. Their code looks nice and clean and well organized, perhaps I’ll give it a try.

My biggest problem with this application of AI is trying to approximate DFT, which itself is an unreliable approximation. The claim is it lets you amortize the expensive DFT to search the space, but it’s also true that especially for inorganic materials, training sets do not appear to promote strong generalization. So you embark on an expensive task to wind up back with unreliable DFT. I think perhaps the best goal would be to try to make DFT itself better, and I have seen impressive albeit computationally expensive approaches, e.g. FermiNet by DeepMind.