Interview with Simone Ciarella: using machine learning to study supercooled liquids

In their paper Dynamics of supercooled liquids from static averaged quantities using machine learning, Simone Ciarella, Massimiliano Chiappini, Emanuele Boattini, Marjolein Dijkstra and Liesbeth M C Janssen introduce a machine-learning approach to predict the complex non-Markovian dynamics of supercooled liquids. In this interview, Simone tells us about supercooled liquids, and how the team used machine learning in their study. Supercooled liquids are liquids that are cooled below their normal freezing point without undergoing a phase transition into a solid state. Imagine you have a glass of water. Normally, when you lower the temperature, the water molecules slow down and arrange themselves into a solid crystal structure, that we call ice, because this structure is very stable.