Thermodynamic stability and critical points in multicomponent mixtures with structured interactions

TL;DR

This paper develops a theoretical framework to analyze the thermodynamic stability and critical points of multicomponent mixtures with structured, non-random interactions, enabling lower-dimensional representations and systematic characterization of critical phenomena.

Contribution

It introduces a mean-field approach for mixtures with structured interactions, allowing coarse-graining and analysis of critical points across diverse models.

Findings

Framework applies to broad class of mean-field models.

Enables lower-dimensional representation of complex mixtures.

Provides systematic characterization of critical points.

Abstract

Theoretical work has shed light on the phase behavior of idealized mixtures of many components with random interactions. But typical mixtures interact through particular physical features, leading to a structured, non-random interaction matrix of lower rank. Here we develop a theoretical framework for such mixtures and derive mean-field conditions for thermodynamic stability and critical behavior. Irrespective of the number of components and features, this framework allows for a generally lower-dimensional representation in the space of features and proposes a principled way to coarse-grain multicomponent mixtures as binary mixtures. Moreover, it suggests a way to systematically characterize different series of critical points and their codimensions in mean-field. Since every pairwise interaction matrix can be expressed in terms of features, our work is applicable to a broad class of mean-field models.