Exploring global landscape of free energy for the coupled Cahn-Hilliard equations

TL;DR

This paper investigates the global free energy landscape of coupled Cahn-Hilliard equations, developing methods to identify saddle points and control trajectories toward desired states, thereby explaining high-yield experimental morphologies.

Contribution

It introduces novel saddle point search and relaxation parameter adjustment methods to analyze and control the free energy landscape in phase separation models.

Findings

Global free energy landscape mapped for a 1D phase separation model

New saddle point search method akin to bifurcation tracking

Strategy for tuning relaxation parameters to control trajectory behaviors

Abstract

Describing the complex landscape of infinite-dimensional free energy is generally a challenging problem. This difficulty arises from the existence of numerous minimizers and, consequently, a vast number of saddle points. These factors make it challenging to predict the location of desired configurations or to forecast the trajectories and pathways leading from an initial condition to the final state. In contrast, experimental observations demonstrate that specific morphologies can be reproducibly obtained in high yield under controlled conditions, even amidst noise. This study investigates the possibility of elucidating the global structure of the free energy landscape and enabling the control of orbits toward desired minimizers without relying on exhaustive brute-force methods. Furthermore, it seeks to mathematically explain the efficacy of certain experimental setups in achieving high-yield outcomes. Focusing on the phase separation of two polymers in a solvent, we conduct a one-dimensional analysis that reveals the global free energy landscape and relaxation-parameter-dependent trajectory behaviors. Two key methodologies are developed: one is a saddle point search method, akin to bifurcation tracking. This method aims to comprehensively identify all saddle points. The other is a strategy that adjusts the relaxation parameters preceding each variable's time derivative, aligning with experimental setups. This approach enables control over trajectory behaviors toward desired structures, overcoming the limitations of steepest descent methods. By tuning these relaxation parameters, uncertainties in trajectory behavior due to inevitable fluctuations can be suppressed. These methodologies collectively offer a mathematical framework that mirrors experimental high-yield phenomena, facilitating a deeper understanding of the underlying mechanisms.