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.