How generative AI can help scientists synthesize complex materials
Generative artificial intelligence models have been used to create enormous libraries of theoretical materials that could help solve all kinds of problems. Now, scientists just have to figure out how to make them. In many cases, materials synthesis is not as simple as following a recipe in the kitchen. Factors like the temperature and length of processing can yield huge changes in a material’s properties that make or break its performance. That has limited researchers’ ability to test millions of promising model-generated materials.
Now, MIT researchers have created an AI model that guides scientists through the process of making materials by suggesting promising synthesis routes. In a new paper, they showed the model delivers state-of-the-art accuracy in predicting effective synthesis pathways for a class of materials called zeolites, which could be used to improve catalysis, absorption, and ion exchange processes. Following its suggestions, the team synthesized a new zeolite material that showed improved thermal stability.
The researchers believe their new model could break the biggest bottleneck in the materials discovery process.
“To use an analogy, we know what kind of cake we want to make, but right now we don’t know how to bake the cake,” says lead author Elton Pan, a PhD candidate in MIT’s Department of Materials Science and Engineering (DMSE). “Materials synthesis is currently done through domain expertise and trial and error.”
The paper describing the work appears today in Nature Computational Science. Joining Pan on the paper are Soonhyoung Kwon ’20, PhD ’24; DMSE postdoc Sulin Liu; chemical engineering PhD student Mingrou Xie; DMSE postdoc Alexander J. Hoffman; Research Assistant Yifei Duan SM ’25; DMSE visiting student Thorben Prein; DMSE PhD candidate Killian Sheriff; MIT Robert T. Haslam Professor in Chemical Engineering Yuriy Roman-Leshkov; Valencia Polytechnic University Professor Manuel Moliner; MIT Paul M. Cook Career Development Professor Rafael Gómez-Bombarelli; and MIT Jerry McAfee Professor in Engineering Elsa Olivetti.
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