This technique works best when the crystal is intact, but in many cases, scientists have only a powdered version of the material, which contains random fragments of the crystal. This makes it more challenging to piece together the overall structure. MIT chemists have now come up with a new generative AI model that can make it much easier to determine the structures of these powdered crystals. The prediction model could help researchers characterize materials for use in batteries, magnets, and many other applications. 'Structure is the first thing that you need to know for any material. It's important for superconductivity, it's important for magnets, it's important for knowing what photovoltaic you created. It's important for any application that you can think of which is materials-centric,' says Danna Freedman, the Frederick George Keyes Professor of Chemistry at MIT. Freedman and Jure Leskovec, a professor of computer science at Stanford University, are the senior authors of the...
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