Less is more? New take on machine learning helps us "scale up" phase transitions
Scientists from Tokyo Metropolitan University used super-resolution machine learning to study phase transitions by simulating tiny arrays and then generating a larger estimate. This technique allows for massive computational cost savings, enabling the study of complex materials behavior.