Hard Constraints Steer Generative AI Toward Chemically Valid Materials
MIT PhD student Mouyang Cheng argues that generative models for materials discovery need explicit scientific constraints, not just larger diffusion models. In a Microsoft Research seminar, he describes two approaches: diffusion inpainting that forces generated crystals to contain target structural motifs, and CrysVCD, a valence-constrained framework that generates charge-balanced formulas before predicting structures. His case is that constraints such as motifs, valence and stability screens make generative materials design more useful in a field where data are sparse and chemically invalid samples are easy to produce.
Microsoft Research·Jun 4, 2026·16 min read
