Exploring the Self-Assembly of Glycolipids into Three-Dimensional Network Phases.

Area of Science:

• Supramolecular Chemistry
• Materials Science
• Computational Chemistry

Background:

• Glycolipids are amphiphilic molecules crucial for biological functions.
• They self-assemble into diverse mesophases, including network structures like the double gyroid.
• Understanding glycolipid self-assembly is key for designing novel materials.

Purpose of the Study:

• To develop and validate a two-stage molecular dynamics simulation workflow for probing network phase formation in solvent-free amphiphiles.
• To investigate the stability of predicted network phases at varying temperatures.
• To facilitate the discovery of new network-forming amphiphiles.

Main Methods:

• A two-stage molecular dynamics simulation approach was employed.
• The first stage examined structural evolution from lamellar and hexagonal phases to predict network formation likelihood.
• The second stage used guiding fields to obtain network phase configurations and assessed their stability.
• A convolutional neural network was utilized for morphology assignment.

Main Results:

• The simulation workflow successfully predicted the formation and stability of glycolipid network phases.
• Phase diagrams generated by the workflow aligned well with experimental data from differential scanning calorimetry and X-ray scattering.
• Three specific anomerically pure, water-free glycolipids were studied.

Conclusions:

• The developed workflow provides an efficient method for investigating network phase stability in amphiphiles.
• This approach can accelerate the discovery and design of novel amphiphilic materials with desired self-assembly properties.