Machine learning predicts the glass transition of two-dimensional colloids besides medium-range crystalline order

We employ only the positions of colloidal particles and construct machine learning (ML) models to test the presence of structural order in glass transition for two kinds of two-dimensional (2D) colloids: 2D polydisperse colloids (PC) with medium-range crystalline order (MRCO) and 2D binary colloids (BC) without MRCO. ML models predict the glass transition of 2D colloids successfully without any information on MRCO. Even certain ML models trained with BC predict the glass transition of PC successfully, thus suggesting that universal structural characteristics would exist besides MRCO.