MIT develops AI model to speed up materials synthesis
Researchers say the technology could eventually link AI-driven planning with automated experiments to transform how new materials are developed.
Researchers at the Massachusetts Institute of Technology have developed a generative AI model to guide scientists through the complex process of materials synthesis, a significant bottleneck in materials discovery.
DiffSyn uses diffusion-based AI to suggest multiple synthesis routes for a material, factoring in temperature, reaction time, and precursor ratios. Unlike earlier tools tied to single recipes, DiffSyn reflects the laboratory reality in which multiple pathways can produce the same material.
The system achieved state-of-the-art accuracy on zeolites, a challenging material class used in catalysis and chemical processing. Using DiffSyn’s recommendations, the team synthesised a new zeolite with improved thermal stability, confirming the model’s practical value.
The researchers believe the approach could be extended beyond zeolites to other complex materials, eventually integrating with automated experiments to shorten the path from theoretical design to real-world application dramatically.
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