Coarse-grained molecular dynamics integrated with convolutional neural network for comparing shapes of temperature sensitive bottlebrushes

作     者：Soumil Y.Joshi Samrendra Singh Sanket A.Deshmukh 

作者机构：Department of Chemical EngineeringVirginia TechBlacksburgVA24061USA 

出 版 物：《npj Computational Materials》 (计算材料学（英文）)

年 卷 期：2022年第8卷第1期

页      面：403-414页

核心收录：

学科分类：081704[工学-应用化学] 07[理学] 08[工学] 0817[工学-化学工程与技术] 070305[理学-高分子化学与物理] 080501[工学-材料物理与化学] 0805[工学-材料科学与工程（可授工学、理学学位）] 0703[理学-化学] 

基　　金：This work was supported by GlycoMIP,a National Science Foundation Materials Innovation Platform funded through Cooperative Agreement DMR-1933525 This research also used resources of the National Energy Research Scientific Computing Center(NERSC),a scientific computing facility for the Office of Science in the United States Department of Energy,operated under Contract No.DE-AC02-05CH11231 

主　　题：convolution chains dynamics 

摘      要：Quantification of shape changes in nature-inspired soft material architectures of stimuli-sensitive polymers is critical for controlling their properties but is challenging due to their softness and ***,we have computationally designed uniquely shaped bottlebrushes of a thermosensitive polymer,poly(N-isopropylacrylamide)(PNIPAM),by controlling the length of side chains along the ***-grained molecular dynamics simulations of solvated bottlebrushes were performed below and above the lower critical solution temperature of *** analyses(free volume,asphericity,etc.)show that lengths of side chains and their immediate environments dictate the compactness and bending in these *** further developed 100 unique convolutional neural network models that captured molecular-level features and generated a statistically significant quantification of the similarity between different ***,our study provides insights into the shapes of complex architectures as well as a general method to analyze *** shapes presented here may inspire the synthesis of new bottlebrushes.