论文标题
纳米网：用于预测聚合物矩阵中纳米颗粒分布的机器学习平台
nanoNET: Machine Learning Platform for Predicting Nanoparticles Distribution in a Polymer Matrix
论文作者
论文摘要
聚合物纳米复合材料（PNC）提供了与其组成相关的广泛的热物理特性。但是，由于PNC的巨大组成和化学空间，建立普遍组成的构成关系是一项挑战。在这里，我们解决了这个问题，并开发了一种新方法，以通过名为Nanonet的智能机器学习管道对PNC的组成微观结构关系进行建模。纳米网是基于计算机视觉和图像识别概念的纳米颗粒（NP）分布预测指标。它将无监督的深度学习和回归整合在全自动管道中。我们对PNC进行粗粒细粒的分子动力学模拟，并利用数据来建立和验证纳米。在此框架内，随机森林回归模型预测了潜在空间中PNC中的NPS分布。随后，基于卷积神经网络的解码器将潜在空间表示形式转换为给定PNC中NPS的实际径向分布函数（RDF）。纳米网非常准确地预测许多未知PNC中的NPS分布。该方法非常通用，可以加速对PNC和其他分子系统组成微结构关系的设计，发现和基本理解。
Polymer nanocomposites (PNCs) offer a broad range of thermophysical properties that are linked to their compositions. However, it is challenging to establish a universal composition-property relation of PNCs due to their enormous composition and chemical space. Here, we address this problem and develop a new method to model the composition-microstructure relation of a PNC through an intelligent machine learning pipeline named nanoNET. The nanoNET is a nanoparticles (NPs) distribution predictor, built upon computer vision and image recognition concepts. It integrates unsupervised deep learning and regression in a fully automated pipeline. We conduct coarse-grained molecular dynamics simulations of PNCs and utilize the data to establish and validate the nanoNET. Within this framework, a random forest regression model predicts the NPs distribution in a PNC in a latent space. Subsequently, a convolutional neural network-based decoder converts the latent space representation to the actual radial distribution function (RDF) of NPs in the given PNC. The nanoNET predicts NPs distribution in many unknown PNCs very accurately. This method is very generic and can accelerate the design, discovery, and fundamental understanding of composition-microstructure relations of PNCs and other molecular systems.
