Graph-Based Feature Engineering to Predict the Dynamical Properties of Condensed Matter

TL;DR

This paper introduces a graph theory-based approach to predict dynamical properties in condensed matter, outperforming traditional methods and effectively identifying phase transitions.

Contribution

It presents a novel graph-based feature engineering method that improves prediction of particle mobility and phase transitions in condensed matter systems.

Findings

Graph-based features outperform Euclidean features in predicting mobility.

The method accurately identifies phase transitions.

Topological features provide new insights into condensed matter dynamics.

Abstract

We present a graph theory-based method to characterise flow defects and structural shifts in condensed matter. We explore the connection between dynamical properties, particularly the recently introduced concept of ''softness'', and graph-based features such as centrality and clustering coefficients. These topological features outperform conventional features based on Euclidean metric in predicting particle mobility and allow to correctly identify phase transitions as well. These results provide a new set of computational tools to investigate the dynamical properties of condensed matter systems.