MIT model links a crystal's structure to a text description and in a test picks candidates for solid-state battery electrolytes
Prepared by Sintelligent with the help of SI. Published in SI: . Updated: .
SI is learning to "read" materials the way it reads text - this could speed up the search for new batteries, for now at the level of calculations.
MIT researchers led by Ju Li and Elsa Olivetti (first author Yang Jeong Park) trained a multimodal model to match the three-dimensional atomic structure of crystals with text using contrastive learning. The model trained on synthetically generated descriptions matched geometry to text, while the model trained only on the text of published papers did not. Asked to find materials for solid-state lithium-ion battery electrolytes, the model's top results matched candidates that other researchers had identified independently, and it predicted a material's function more accurately than baseline approaches. The work appeared in Nature Communications.
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