Researchers have developed an AI model that predicts atomic arrangements in crystal structures, streamlining the discovery of new materials for technologies like solar panels and computer chips.
A new artificial intelligence model, CrystaLLM, has been developed to predict how atoms arrange themselves in crystal structures. This breakthrough could accelerate the discovery of new materials used in technologies such as batteries, computer chips, and solar cells.
Created by researchers at the University of Reading and University College London, CrystaLLM operates like AI chatbots, learning the “language” of crystals by analyzing millions of existing crystal structures.
Published today (December 6) in Nature Communications, the system will be made available to the scientific community to support advancements in material discovery.
Breakthrough in Crystal Structure Prediction
Dr. Luis Antunes, who led the research while completing his PhD at the University of Reading, said: “Predicting crystal structures is like solving a complex, multidimensional puzzle where the pieces are hidden. Crystal structure prediction requires massive computing power to test countless possible arrangements of atoms.
“CrystaLLM offers a breakthrough by studying millions of known crystal structures to understand patterns and predict new ones, much like an expert puzzle solver who recognizes winning patterns rather than trying every possible move.”
A New Approach to Material Science
The current process for figuring out how atoms will arrange themselves into crystals relies on time-consuming computer simulations of the physical interactions between the atoms. CrystaLLM works in a simpler way. Instead of using complex physics calculations, it learns by reading millions of crystal structure descriptions contained in Crystallographic Information Files – the standard format for representing crystal structures.
CrystaLLM treats these crystal descriptions just like text. As it reads each description, it predicts what comes next, gradually learning patterns about how crystals are structured. The system was never taught any physics or chemistry rules, but instead figured them out on its own. It learned things like how atoms arrange themselves and how their size affects the crystal’s shape, just from reading these descriptions.
Practical Applications and Accessibility
When tested, CrystaLLM could successfully generate realistic crystal structures, even for materials it had never seen before.
The research team has created a free website where researchers can use CrystaLLM to generate crystal structures. The integration of this model within crystal structure prediction workflows could speed up the development of new materials for technologies like better batteries, more efficient solar cells, and faster computer chips.
Reference: “Crystal structure generation with autoregressive large language modeling” by Luis M. Antunes, Keith T. Butler and Ricardo Grau-Crespo, 6 December 2024, Nature Communications.
DOI: 10.1038/s41467-024-54639-7
Never miss a breakthrough: Join the SciTechDaily newsletter.
Follow us on Google and Google News.