多组分聚合物沉淀的数值模拟、聚类和预测

IF 2.4 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Pavan K Inguva, L. Mason, Indranil Pan, Miselle Hengardi, O. Matar
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引用次数: 4

摘要

摘要多组分聚合物系统在有机光伏和药物递送应用中备受关注,其中不同的形态影响性能。在成分知情预测工具的推动下,对形态分类的更好理解将有助于聚合物工程实践。我们使用改进的Cahn–Hilliard模型来模拟聚合物沉淀。这种基于物理的模型需要高性能的计算,以防止在工程环境中进行快速原型设计和迭代。为了降低所需的计算成本,我们将机器学习(ML)技术应用于模拟聚合物共混物图像的聚类和后续预测,并结合模拟。以这种方式集成ML和模拟减少了绘制作为输入参数函数的聚合物共混物形态所需的模拟次数,并且还生成了可供其他人用于此目的的数据集。我们通过主成分分析和自动编码器技术探索降维,并分析由此产生的形态聚类。随后,根据物种摩尔分数和相互作用参数输入,使用高斯过程分类的监督ML来预测形态聚类。人工模式聚类产生了最好的结果,但ML技术能够预测聚合物共混物的形态,准确率≥90%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Numerical simulation, clustering, and prediction of multicomponent polymer precipitation
Abstract Multicomponent polymer systems are of interest in organic photovoltaic and drug delivery applications, among others where diverse morphologies influence performance. An improved understanding of morphology classification, driven by composition-informed prediction tools, will aid polymer engineering practice. We use a modified Cahn–Hilliard model to simulate polymer precipitation. Such physics-based models require high-performance computations that prevent rapid prototyping and iteration in engineering settings. To reduce the required computational costs, we apply machine learning (ML) techniques for clustering and consequent prediction of the simulated polymer-blend images in conjunction with simulations. Integrating ML and simulations in such a manner reduces the number of simulations needed to map out the morphology of polymer blends as a function of input parameters and also generates a data set which can be used by others to this end. We explore dimensionality reduction, via principal component analysis and autoencoder techniques, and analyze the resulting morphology clusters. Supervised ML using Gaussian process classification was subsequently used to predict morphology clusters according to species molar fraction and interaction parameter inputs. Manual pattern clustering yielded the best results, but ML techniques were able to predict the morphology of polymer blends with ≥90% accuracy.
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来源期刊
DataCentric Engineering
DataCentric Engineering Engineering-General Engineering
CiteScore
5.60
自引率
0.00%
发文量
26
审稿时长
12 weeks
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