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.
A geothermal de-icing system designed by researchers at The University of Texas at Arlington kept a test bridge mostly clear of snow and ice during the sub-freezing winter storm in February. Now the research team will install its system on an in-service bridge to see how it performs under operational conditions.
Our results suggest that olivine and wadsleyite show dry transformation kinetics even in wet slabs. It is therefore possible that olivine transformation as a cause of deep-focus earthquakes and large slab deformation creating stagnant slabs could occur in the water-undersaturated wet slabs. These processes could be caused jointly by dehydration of hydrous minerals and the subsequent rapid phase transformation when the dehydration starts at lower temperatures than the phase transformation.