机器学习在高分子材料基因组研究中的进展与挑战

宫祥瑞; 蒋滢

doi:10.11777/j.issn1000-3304.2022.22094

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机器学习在高分子材料基因组研究中的进展与挑战

综述 | 更新时间：2024-10-08

- 机器学习在高分子材料基因组研究中的进展与挑战
- Advances and Challenges of Machine Learning in Polymer Material Genomes
- 高分子学报 2022年53卷第11期页码：1287-1300
- 作者机构：
  
  1.北京航空航天大学，化学学院，北京 100191
  2.北京航空航天大学，仿生智能界面科学与技术教育部重点实验室，北京 100191
  3.北京航空航天大学，软物质物理及其应用中心，北京 100191
  4.北京航空航天大学，北京生物医学工程高精尖创新中心，北京 100191
- 作者简介：
  
  [ "蒋滢，男，1978年生. 研究员，博士生导师，国家自然科学基金委优秀青年基金获得者. 2001年毕业于浙江师范大学物理教育专业，2006年于中国科学技术大学高分子科学与工程系高分子物理与化学专业获博士学位，2006~2009年先后在美国科罗拉多州立大学、中国科学技术大学从事博士后研究工作，2009~2013年在加拿大滑铁卢大学天文与应用物理系生物物理专业攻读博士，获博士学位. 2013年在温州大学物理系工作，2014年至今，在北京航空航天大学化学学院历任副教授、研究员. 主要从事聚合物材料表界面效应的理论研究，运用和发展介观尺度的理论和模拟方法，并结合机器学习，研究高分子与表界面相互作用下体系的平衡态和非平衡态性质，揭示其在影响聚合物体系性质和功能方面的物理机制." ]
- 基金信息：
  
  国家自然科学基金(基金号 22073004)
- DOI：10.11777/j.issn1000-3304.2022.22094
  中图分类号：
- 纸质出版日期：2022-11-20，
  
  网络出版日期：2022-07-22，
  
  收稿日期：2022-03-25，
  
  录用日期：2022-04-18
- 稿件说明：
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宫祥瑞,蒋滢.机器学习在高分子材料基因组研究中的进展与挑战[J].高分子学报,2022,53(11):1287-1300.

Gong Xiang-rui,Jiang Ying.Advances and Challenges of Machine Learning in Polymer Material Genomes[J].ACTA POLYMERICA SINICA,2022,53(11):1287-1300.
宫祥瑞,蒋滢.机器学习在高分子材料基因组研究中的进展与挑战[J].高分子学报,2022,53(11):1287-1300. DOI： 10.11777/j.issn1000-3304.2022.22094.

Gong Xiang-rui,Jiang Ying.Advances and Challenges of Machine Learning in Polymer Material Genomes[J].ACTA POLYMERICA SINICA,2022,53(11):1287-1300. DOI： 10.11777/j.issn1000-3304.2022.22094.

摘要

机器学习对高分子材料基因组的研究与发展有着重要作用.它通过对高分子材料结构描述符的设计与筛选、材料数据库的完善以及聚合物分子结构标识符的发展等方法，有效地构建分子链的化学组成、构象与其聚集态结构、宏观性能之间的联系.本文梳理了近几年机器学习方法在高分子材料科学领域的研究进展，总结了一些常用机器学习算法的应用与研究成果，同时，也针对算法所需的数据量较大等问题，特别指出了应对数据量较少或是数据成本昂贵时的解决方案.根据当前研究进展，整理了机器学习方法在高分子材料科学领域中应用的难题与挑战.

Abstract

Machine learning (ML) plays an important role in the investigation and development of polymer material genomes. The success of ML-based studies strongly depends on the design and selection of feature descriptors

which reasonably portray chemical and structural characteristics of polymer materials. In this review

we elucidate a few of descriptors commonly utilized for effectively constructing the link between polymer structures

chemical compositions and aggregate structures

macroscopic properties. In addition

the database

especial for the polymer materials

is also explicitly listed

although the continuous development of specific database is still in a large demand. The research progress of ML methods in the field of polymer materials in recent years is reviewed

as well as successful applications and achievements. In particular

the solutions to deal with the small amount of data or the high cost of expensive data are also presented. According to the current research progress

the difficulty and challenge of ML applications in the field of polymer materials are discussed as well.

关键词

高分子材料基因组机器学习结构与性能关系目标性能预测与优化多目标优化

Keywords

Polymer material genomesMachine learningRelations between structures and propertiesPrediction and optimization of target propertiesMulti-objective optimization

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