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。

粒子形状系统中的复杂局部环境分类通过形状对称编码的数据增强

Classification of complex local environments in systems of particle  shapes through shape-symmetry encoded data augmentation

We propose reinforcement learning to control the dynamical self-assembly of
the dodecagonal quasicrystal (DDQC) from patchy particles. The patchy particles
have anisotropic interactions with other particles and form DDQC. However,
their structures at steady states are significantly influenced by the kinetic
pathways of their structural formation. We estimate the best policy of
temperature control trained by the Q-learning method and demonstrate that we
can generate DDQC with few defects using the estimated policy. The temperature
schedule obtained by reinforcement learning can reproduce the desired structure
more efficiently than the conventional pre-fixed temperature schedule, such as
annealing. To clarify the success of the learning, we also analyse a simple
model describing the kinetics of structural changes through the motion in a
triple-well potential. We have found that reinforcement learning autonomously
discovers the critical temperature at which structural fluctuations enhance the
chance of forming a globally stable state. The estimated policy guides the
system toward the critical temperature to assist the formation of DDQC.

我们提出了强化学习来控制补丁颗粒的动力自组装十二边形准晶体（DDQC）。研究发现，通过训练温度控制的最佳策略，我们能够使用估计的策略生成缺陷较少的 DDQC。与传统的固定温度表如退火等相比，通过强化学习获得的温度计划能更高效地复现所需的结构。同时，我们发现强化学习能够自主地发现结构波动增强实现全局稳态的临界温度。估计的策略引导系统朝向临界温度以促进 DDQC 的形成。