AI diffusion model maps multiple synthesis routes to speed up materials discovery
MIT researchers trained a diffusion-based AI, DiffSyn, on 23,000 material-synthesis recipes to map a target structure to multiple viable synthesis routes, enabling rapid exploration of temperatures, times, and precursor ratios. In tests with zeolites, DiffSyn suggested promising pathways and helped synthesize a new zeolite with improved thermal stability, reducing trial‑and‑error to a rapid initial search. This one‑to‑many approach better matches experimental reality and could extend to other materials, though high‑quality data remain a bottleneck; future work includes autonomous experiments and broader material classes.
