Solvable models of Glass Transition

TL;DR

This paper introduces solvable statistical models that connect local structure to the glass transition temperature in various network glasses, validated against experimental data.

Contribution

It presents a new class of solvable models that relate local structural features to the glass transition temperature across different glass compositions.

Findings

Models successfully predict glass transition temperatures for binary, ternary, and multicomponent glasses.

Predictions show good agreement with experimental data.

Models highlight the role of local structure in glass transition phenomena.

Abstract

Simple statistical agglomeration models can provide a universal link between the local structure and the glass transition temperature in network glasses. We first stress the physical features of the models and the hypothesis made, and then show how to define the glass transition temperature. The models are applied to various types of binary, ternary and multicomponent chalcogenide glass networks and the predictions compared to experimental data.