(451j) Order Parameter-Free Classification of Complex Local Environments of Shape Particles

Detecting and analyzing the local environment is crucial for investigating the dynamical processes of crystal nucleation and shape colloidal particle self-assembly. Recent developments in machine learning provide a promising avenue for better order parameters in complex systems that are challenging to study using traditional approaches. However, the application of machine learning to self-assembly on shape particles is still underexplored. To address this gap, we propose a simple yet powerful approach that involves training a multilayer perceptron (MLP) as a local environment classifier for shape particles, using input features such as particle distances and orientations. Our MLP model is trained in a supervised manner with shape symmetry-encoded data augmentation technique, without the need for any traditional order parameters or symmetry functions. We evaluate the performance of our model on four different scenarios involving self-assembly of cubic structures, 2-dimensional and 3-dimensional patchy shape particle systems, hexagonal bipyramids with varying aspect ratios, and truncated shapes with different degrees of truncation. The proposed training process and data augmentation technique are both straightforward and highly interpretable, enabling easy application of the model to other processes involving particle orientations. Our work thus presents a valuable tool for the investigation of self-assembly processes on shape particles, with potential applications in structure identification of molecular or coarse-grained systems.