Self-assembly prediction of architecture-controlled bottlebrush copolymers in solution using graph convolutional networks-Reference-Cited by-同舟云学术

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Self-assembly prediction of architecture-controlled bottlebrush copolymers in solution using graph convolutional networks

Published:2024 Issue:25 Volume:20 Page:4905-4915
ISSN:1744-683X
Container-title:Soft Matter
language:en
Short-container-title:Soft Matter

Author:

Hwang Wooseop¹,Kwon Sangwoo²^ORCID,Lee Won Bo²^ORCID,Kim YongJoo¹^ORCID

Affiliation:

1. Department of Materials Science and Engineering, Korea University, Seoul 02841, Republic of Korea

2. School of Chemical and Biological Engineering, Institute of Chemical Processes, Seoul National University, Seoul 08826, Republic of Korea

Abstract

Self-assembly behavior of architecture-controlled bottlebrush copolymers are predicted using graph convolutional networks

Funder

National Research Foundation of Korea

Ministry of Science and ICT, South Korea

Korea University

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2024/SM/D4SM00453A

Reference45 articles.

1. Self-assembly in aqueous block copolymer solutions

2. Directing the self-assembly of block copolymers

3. Self-assembly of block copolymers

4. Toward ‘smart’ nano-objects by self-assembly of block copolymers in solution

5. Block copolymer solution self‐assembly: Recent advances, emerging trends, and applications

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