Programmable phase behavior in fluids with designable interactions

TL;DR

This paper presents a convex optimization-based method to design molecular interactions that produce desired complex phase diagrams in multicomponent fluids, verified through simulations.

Contribution

It introduces a novel inverse design approach for phase behavior in fluids, enabling programmable control over complex phase diagrams.

Findings

Successfully designed interactions for target phase diagrams

Validated designs with molecular simulations

Applicable to biopolymer and colloidal mixtures

Abstract

We introduce a method for solving the "inverse" phase equilibria problem: How should the interactions among a collection of molecular species be designed in order to achieve a target phase diagram? Using techniques from convex optimization theory, we show how to solve this problem for phase diagrams containing a large number of components and many coexisting phases with prescribed compositions. We apply our approach to commonly used mean-field models of multicomponent fluids and then use molecular simulations to verify that the designed interactions result in the target phase diagrams. Our approach enables the rational design of "programmable" fluids, such as biopolymer and colloidal mixtures, with complex phase behavior.