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 systems of particle shapes is still underexplored.
To address this gap, we propose a simple, physics-agnostic, yet powerful
approach that involves training a multilayer perceptron (MLP) as a local
environment classifier for systems of particle shapes, using input features
such as particle distances and orientations. Our MLP classifier is trained in a
supervised manner with a shape symmetry-encoded data augmentation technique
without the need for any conventional roto-translations invariant symmetry
functions. We evaluate the performance of our classifiers on four different
scenarios involving self-assembly of cubic structures, 2-dimensional and
3-dimensional patchy particle shape 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 flexible, enabling easy application of the classifier to
other processes involving particle orientations. Our work thus presents a
valuable tool for investigating self-assembly processes on systems of particle
shapes, with potential applications in structure identification of any
particle-based or molecular system where orientations can be defined.

使用机器学习方法，我们提出一种简单而强大的多层感知器 (MLP) 分类器，通过训练粒子距离和方向等输入特征，能够检测和分析晶核形成和胶体颗粒自组装的局部环境，该方法具有物理无关性和数据扩充技术，对于研究粒子形状系统的自组装过程提供了有价值的工具。