Estimating glass transition temperature and related dynamics of molecular glass formers combining artificial neural networks and disordered systems theory

Abstract

Glass transition temperature and related dynamics play an essential role in amorphous materials research since many of their properties and functionalities depend on molecular mobility. However, the temperature dependence of the structural relaxation time for a given glass former is only experimentally accessible after synthesizing it, implying a time-consuming and costly process. In this work, we propose combining artificial neural networks and disordered systems theory to estimate the glass transition temperature and the temperature dependence of the main relaxation time based on the knowledge of the molecule's chemical structure. This approach provides a way to assess the dynamics of molecular glass formers, with reasonable accuracy, even before synthesizing them. We expect this methodology to boost industrial development, save time and resources, and accelerate the scientific understanding of structure-properties relationships. • New joint theoretical and numerical approach to estimate the molecular dynamics • Machine learning for understanding structure-property relationships • Artificial neural networks for the design and development of new materials • Combining artificial neural networks and disordered systems theory • Estimating glass transition temperature and related dynamics of molecular glass formers