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Data-storage technologies depend on materials that sustain magnetic properties at high temperature. While researchers have a range of such materials to work with, theory suggests that the known options are but a small fraction of the high-temperature magnets that are possible. To speed up the discovery and design of new high-temperature magnets, James Nelson and Stefano Sanvito of Trinity College in Ireland have developed several machine-learning models that can predict the temperature at which a material demagnetizes—its Curie temperature—from its chemical composition.

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Category: Science