Ayush Kumar, Seth Abhishek, Patra Tarak K
Department of Chemical Engineering and Center for Atomistic Modeling and Materials Design, Indian Institute of Technology Madras, Chennai, TN 600036, India.
Soft Matter. 2023 Jul 26;19(29):5502-5512. doi: 10.1039/d3sm00567d.
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 relationship in PNCs due to their wide-ranging 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 distribution of NPs 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 relationships in PNCs and other molecular systems.
聚合物纳米复合材料（PNCs）具有一系列与其组成相关的热物理性质。然而，由于PNCs的组成和化学空间范围广泛，建立通用的组成-性质关系具有挑战性。在此，我们解决这一问题，并开发了一种新方法，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。nanoNET是一种基于计算机视觉和图像识别概念构建的纳米颗粒（NPs）分布预测器。它在一个完全自动化的管道中集成了无监督深度学习和回归。我们对PNCs进行粗粒度分子动力学模拟，并利用这些数据建立和验证nanoNET。在此框架内，一个随机森林回归模型预测PNC中NPs在潜在空间中的分布。随后，基于卷积神经网络的解码器将潜在空间表示转换为给定PNC中NPs的实际径向分布函数（RDF）。nanoNET能非常准确地预测许多未知PNC中NPs的分布。该方法非常通用，可加速对PNCs和其他分子系统中组成-微观结构关系的设计、发现及基础理解。
