Less is more? New take on machine learning helps us “scale up” phase transitions

Researchers from Tokyo Metropolitan University have enhanced “super-resolution” machine learning techniques to study phase transitions. They identified key features of how large arrays of interacting “particles” behave at different temperatures by simulating tiny arrays before using a convolutional neural network to generate a good estimate of what a larger array would look like using “correlation” configurations. The massive saving in computational cost may realize unique ways of understanding how materials behave.
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