Machine learning caging order parameters in glasses
Machine learning caging order parameters in glasses
Non-equilibrium phase transitions in glassy systems are often indicated by a dramatic change of dynamics, accompanied by subtle and ambiguous structural signatures. This fact has motivated a number of recent studies attempting to pinpoint predictors that correlate structural order to dynamics in glasses, via the application of modern machine learning …