高分子材料大数据研究：共性基础、进展及挑战

刘伦洋; 丁芳; 李云琦

doi:10.11777/j.issn1000-3304.2021.21360

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高分子材料大数据研究：共性基础、进展及挑战

综述 | 更新时间：2024-10-08

- 高分子材料大数据研究：共性基础、进展及挑战
- Big Data Approach on Polymer Materials: Fundamental, Progress and Challenge
- 高分子学报 2022年53卷第6期页码：564-580
- 作者机构：
  
  1.中国科学院长春应用化学研究所高分子化学与物理国家重点实验室长春 130022
  2.中国科学技术大学应用化学与工程学院合肥 230026
- 作者简介：
  
  [ "李云琦，男，1980年生. 研究员，博士生导师，获中国科学院人才计划支持. 2001年毕业于南京大学化学系，2007年在中国科学院长春应用化学研究所高分子物理与化学国家重点实验室获博士学位，之后在美国堪萨斯大学分子生物中心和应用生物信息实验室、美国罗格斯大学食品系开展了3期博士后研究，2013年回国. 在高分子科学、结构生物学、生物信息学、食品科学及材料信息学等多个领域有深入研究经历，聚焦体系的组成-工艺-结构-性质-性能的多尺度定量关联，研究了蛋白质复合物和高分子凝胶、分离膜、弹性体等体系，将先进材料的基础研究与前沿应用结合起来，探索出了一套理论、模拟、实验与大数据有机结合的多学科交叉方法. 目前主要从事高分子材料的结构与大数据研究." ]
- 基金信息：
  
  国家自然科学基金(基金号 ┣21774128);U1832177, 51988102, 22173094┫;中国科学院前沿科学重点研究项目(QYZDY-SSW-SLH027)
- DOI：10.11777/j.issn1000-3304.2021.21360
  中图分类号：
- 纸质出版日期：2022-06-20，
  
  网络出版日期：2022-03-14，
  
  收稿日期：2021-11-23，
  
  修回日期：2022-01-07，
- 稿件说明：
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刘伦洋,丁芳,李云琦.高分子材料大数据研究：共性基础、进展及挑战[J].高分子学报,2022,53(06):564-580.

Liu Lun-yang,Ding Fang,Li Yun-qi.Big Data Approach on Polymer Materials: Fundamental, Progress and Challenge[J].ACTA POLYMERICA SINICA,2022,53(06):564-580.
刘伦洋,丁芳,李云琦.高分子材料大数据研究：共性基础、进展及挑战[J].高分子学报,2022,53(06):564-580. DOI： 10.11777/j.issn1000-3304.2021.21360.

Liu Lun-yang,Ding Fang,Li Yun-qi.Big Data Approach on Polymer Materials: Fundamental, Progress and Challenge[J].ACTA POLYMERICA SINICA,2022,53(06):564-580. DOI： 10.11777/j.issn1000-3304.2021.21360.

摘要

介绍了作为一种新的认知范式，大数据研究常见和前沿算法及其应用在高分子材料研究中的共性基础，围绕材料的基础与应用研究聚焦的定量组成-工艺-结构-性质-性能关系，剖析了该关系中的要素和可数值化、定量化的资源和途径. 进而系统介绍近4年在高分子材料的合成与自组装、机械热性质、光电声磁性质、分离性质和加工性质等方面大数据研究的一些典型进展，梳理了当前高分子材料大数据研究的难题和挑战，对这一新兴快速发展方向和一段时间内可能的突破进行了展望.

Abstract

Big data approach

a new paradigm for data-driven wisdom paces together with conventional experimental

theoretical and simulation ways. The core in the application of big data study in material researches is at the composition-process-structure-property-performance relationship (CPSPPr). The digitalization and computational efforts

concepts

tools and resources to enable the subterms in the CPSPPr to cover fundamental research and scalable production will be presented. The big data approach can fully utilize the merits for the "black-box" of emerging machine learning algorithms

which allows for the construction of much more quantitative correlations beyond conventional rationality. It provides fantastic wisdom in reward for the discovery and the manufacture of new materials from the inherently frustrated multiple scales

broadly distributed

weak but accumulatively-strong response of polymers. We hereby reviewed such representative progresses in the innovation of polymer materials wholly or partially using big data approach in the last four years. These progresses are grouped as: polymer synthesis and self-assembly

mechanical and thermal properties

optic-electric-magnetic-acoustic properties and membranes for separation. The overall progress for the application of big data approach in the research of polymer materials is lagging in the comparison with that in inorganic or small-molecular materials. We then enumerated a number of challenges and possible short-term breakthroughs before the dawn of burst for big data in the reshape of the research and the production of polymer materials.

关键词

高分子材料大数据组成-工艺-结构-性质-性能关系计算辅助材料设计

Keywords

Polymer materialBig dataComposition-process-structure-property-performance relationshipComputer aided material design

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