高分子材料基因组——高分子研发的新方法

都仕; 张宋奇; 王立权; 林嘉平; 杜磊

doi:10.11777/j.issn1000-3304.2021.21404

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高分子材料基因组——高分子研发的新方法

综述 | 更新时间：2024-10-08

- 高分子材料基因组——高分子研发的新方法
- Polymer Genome Approach: A New Method for Research and Development of Polymers
- 高分子学报 2022年53卷第6期页码：592-607
- 作者机构：
  
  华东理工大学材料科学与工程学院上海 200237
- 作者简介：
  
  [ "王立权，男，1982年生，华东理工大学副教授，博士生导师. 2005年华东理工大学本科毕业，2011年华东理工大学博士毕业. 毕业后在华东理工大学任教至今，期间(2016年2月~2017年2月)在加州理工学院作访问学者，主要从事针对高性能树脂设计的材料基因工程、高分子理论计算与模拟等研究工作." ]
  [ "林嘉平，男，1964年生，华东理工大学特聘教授. 1986年、1989年上海交通大学本科、硕士毕业，1993年华东化工学院博士毕业，1993年6月~1995年6月日本东京工业大学博士后研究，1995年6月~1997年8月奥地利林茨大学博士后研究. 1997年华东理工大学任教至今. 任华东理工大学学术、学位委员会委员，国务院学位委员会第7、8届学科评议组(材料科学与工程组)成员、中国材料研究学会理事、中国化学会应用化学学科委员会委员、英国皇家化学会会士. 任《功能高分子学报》主编；《Polymer International》《高分子学报》《高分子材料科学与工程》等刊物的编委. 主要从事高分子理论模拟、高分子自组装和生物医用高分子、高分子材料基因组的研究，先后承担了国家自然科学基金重点、面上项目、军科委基础加强计划重点项目、教育部科学技术研究重大项目、国防科工局重大项目等多项科研项目. 2004年入选教育部新世纪优秀人才，2005年获得国务院特殊津贴，2008年入选上海市领军人才，2009年获得国家杰出青年基金，入选国家新世纪百千万人才工程，入选上海市优秀学科带头人." ]
- 基金信息：
  
  国家自然科学基金(基金号 ┣51833003;22173030);21975073, 21774032, 51621002┫
- DOI：10.11777/j.issn1000-3304.2021.21404
  中图分类号：
- 纸质出版日期：2022-06-20，
  
  网络出版日期：2022-04-07，
  
  收稿日期：2021-12-31，
  
  录用日期：2022-02-14
- 稿件说明：
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都仕,张宋奇,王立权等.高分子材料基因组——高分子研发的新方法[J].高分子学报,2022,53(06):592-607.

Du Shi,Zhang Song-qi,Wang Li-quan,et al.Polymer Genome Approach: A New Method for Research and Development of Polymers[J].ACTA POLYMERICA SINICA,2022,53(06):592-607.
都仕,张宋奇,王立权等.高分子材料基因组——高分子研发的新方法[J].高分子学报,2022,53(06):592-607. DOI： 10.11777/j.issn1000-3304.2021.21404.

Du Shi,Zhang Song-qi,Wang Li-quan,et al.Polymer Genome Approach: A New Method for Research and Development of Polymers[J].ACTA POLYMERICA SINICA,2022,53(06):592-607. DOI： 10.11777/j.issn1000-3304.2021.21404.

摘要

材料基因组旨在将计算工具、数据库和实验工具有机结合，缩短研发时间，提高材料研发效率. 高分子材料因其结构独特性和复杂性阻碍了材料基因组在高分子材料领域的发展. 目前，国内外学者在高通量筛选高分子化学结构策略和构建性能预测方法等方面开展了尝试，并取得了一些成果. 本文总结和评述了当前利用代理量方法和机器学习预测模型实现高分子材料基因组的进展，利用可计算的量代理宏观性能的代理量法和利用机器学习模型预测材料性能的方法在一定程度上克服了高分子复杂性的影响.在此基础上，系统地介绍了数据挖掘或模型构建的方法以及运用这些模型筛选不同类型高分子的思路，着重探讨了方法构建和材料筛选背后的思想以及对各类问题的解决措施.最后，探讨了当前高分子材料基因组发展中所面临的主要挑战，并展望了高分子材料基因组的未来发展方向.

Abstract

The materials genome approach (MGA)

which can accelerate the research and development of new materials

via

combining computer technology

database technology

and experiments

has attracted considerable attention from academia and industry. However

establishing the structure-property relationship of polymer is more complicated than those of metal and inorganic materials because of the complex structural features such as chain architecture

chain configuration

chain conformation

and chain aggregation. The difficulty in building the structure-property relationship has hindered the development of MGA in polymers. Recently

there have been increasing studies on the rational design of advanced polymers by MGA. This review summarizes research progresses on the MGA of polymer

including establishing structure-property relationships that can predict polymer properties

exploring the vast chemical space of polymers

and rationally designing polymer structures. Calculating the key features that correlate with the desired properties from data mining is one of the ways to screen promising polymers. Alternatively

machine learning can construct structure-property relationships automatically based on databases. The model based on machine learning can apply to the forward and inverse design of advanced polymers. These two prevalent methods are presented. The review systematically introduces the methods of data mining or model construction and the ideas of screening different types of polymers by using these models and focuses on the ideas behind method construction and material screening and the solutions to various problems. In addition

the challenges faced by the development of polymer MGA are also outlined. To push forward the research on polymer MGA

we suggest paying more attention to introducing more efficient machine learning technology

establishing more comprehensive polymer databases

and developing high-throughput experimental technology in the future.

关键词

材料基因组高分子机器学习理论模拟

Keywords

Material genome approachPolymerMachine learningSimulation

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