  Amphiphilic molecules spontaneously form self-assembly structures based on
physical conditions such as molecular structure, concentration, and
temperature. These structures exhibit various useful functions according to
their morphology. The concept of the critical packing parameter serves to
correlate self-organized structures with chemical composition. However, unless
both molecular arrangement and self-assembly patterns are understood, direct
computational utilization for molecular design remains challenging. In this
study, we attempt to predict the self-assembled structure of a molecule
directly from its chemical structure and analyze factors influencing it using
machine learning. Dissipative particle dynamics simulations were used to
reproduce many self-assembly structures composed of various chemical
structures, and their critical packing parameters were calculated. A machine
learning model was built using the chemical structures as input data and the
critical packing parameters as output data.As a result, both Random Forest and
a type of Recurrent Neural Network known as GRU demonstrated high predictive
accuracy. It has been revealed through feature importance analysis and
dependence on sample size that the amphiphilic nature of molecules
significantly influences the self-assembly structures. Additionally, the
importance of selecting an appropriate molecular structure representation for
each algorithm has been emphasized. The results of this research will help to
further streamline product development in the fields of materials science,
materials chemistry, and medical materials.
