  Amphiphilic molecules spontaneously form self-assembled structures of various
shapes depending on their molecular structures, the temperature, and other
physical conditions. The functionalities of these structures are dictated by
their formations and their properties must be evaluated for reproduction using
molecular simulations. However, the assessment of such intricate structures
involves many procedural steps. This study investigates the potential of
machine-learning models to extract structural features from mesoscale
non-ordered self-assembled structures, and suggests a methodology in which
machine-learning models for the structural analysis of self-assembled
structures are trained on particle types and coordinate data. In the proposed
approach, graph neural networks are utilised to extract local structural data
for analysis. In simulations using several hundred self-assembled structures of
up to 4050 coarse-grained particles, local structures are successfully
extracted and classified with up to 78.35 % accuracy. As the machine-learning
models learn structural characteristics without the need for human-made feature
engineering, the proposed method has important potential applications in the
field of materials science.
