Convolutional neural network-based colloidal self-assembly state classification
Convolutional neural network-based colloidal self-assembly state classification
复制标题
基于卷积神经网络的胶体自组装状态分类
DOI：
10.1039/d3sm00139c
复制
发表时间：
2023
期刊：
影响因子：
3.4
通讯作者：
Tang, Xun
中科院分区：
文献类型：
--
作者：
Lizano, Andres;Tang, Xun
胶体自组装是制造先进超材料的可行方法。当粒子的物理化学性质影响组装结构的性质时，粒子的形态也是一个关键的决定因素。胶体自组装状态分类通常是通过有序参数来实现的，有序参数通常是通过非平凡的探索和验证来定义的聚合变量。在这里，我们提出了一个基于图像的框架来分类二维胶体自组装系统的状态。该框架利用无监督学习的深度学习算法进行状态分类，并利用基于监督学习的卷积神经网络进行状态预测。利用实验验证的布朗动力学仿真数据建立了神经网络模型。结果表明，该方法在区分空缺陷状态和有序状态方面具有令人满意的性能，可以与常用的有序参数相媲美，甚至优于它们。考虑到该方法基于数据的性质，我们预计它的一般适用性和潜在的自动化，在不同和复杂的系统中，图像或粒子协调采集是可行的。
Colloidal self-assembly is a viable solution to making advanced metamaterials. While the physicochemical properties of the particles affect the properties of the assembled structures, particle configuration is also a critical determinant factor. Colloidal self-assembly state classification is typically achieved with order parameters, which are aggregate variables normally defined with nontrivial exploration and validation. Here, we present an image-based framework to classify the state of a 2-D colloidal self-assembly system. The framework leverages deep learning algorithms with unsupervised learning for state classification and a supervised learning-based convolutional neural network for state prediction. The neural network models are developed using data from an experimentally validated Brownian dynamics simulation. Our results demonstrate that the proposed approach gives a satisfying performance, comparable and even outperforming the commonly used order parameters in distinguishing void defective states from ordered states. Given the data-based nature of the approach, we anticipate its general applicability and potential automatability to different and complex systems where image or particle coordination acquisition is feasible.