A Microscopic Theory of Softness in Supercooled Liquids

TL;DR

This paper introduces a microscopic measure of softness in liquids, demonstrating its correlation with dynamics across various liquids and providing a theoretical framework linking it to machine learning-based softness predictions.

Contribution

It develops a mean-field potential-based softness measure and establishes a universal, quantitative relationship between softness and liquid dynamics, bridging theory and machine learning approaches.

Findings

Softness is linearly proportional to temperature.

Softness is inversely proportional to activation barriers.

Fragile liquids show strong softness dependence, strong liquids show weak dependence.

Abstract

We introduce a new measure of the structure of a liquid which is the softness of the mean-field potential developed by us earlier. We find that this softness is sensitive to small changes in the structure. We then study its correlation with the supercooled liquid dynamics. The study involves a wide range of liquids (fragile, strong, attractive, repulsive, and active) and predicts some universal behaviours like the softness is linearly proportional to the temperature and inversely proportional to the activation barrier of the dynamics with system dependent proportionality constants. We write down a master equation between the dynamics and the softness parameter and show that indeed the dynamics when scaled by the temperature and system dependent parameters show a data collapse when plotted against softness. The dynamics of fragile liquids show a strong softness dependence whereas that of strong liquids show a much weaker softness dependence. We also connect the present study with the earlier studies of softness involving machine learning (ML) thus providing a theoretical framework for understanding the ML results.