机器学习方法在高分子玻璃化研究中的应用

杨镇岳; 聂文建; 刘伦洋; 徐晓雷; 夏文杰; 徐文生

doi:10.11777/j.issn1000-3304.2022.22367

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机器学习方法在高分子玻璃化研究中的应用

综述 | 更新时间：2023-03-08

- 机器学习方法在高分子玻璃化研究中的应用
- Applications of Machine Learning Methods in the Studies of Polymer Glass Formation
- 高分子学报 2023年54卷第4期页码：432-450
- 作者机构：
  
  1.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长春 130022
  2.Department of Civil， Construction and Environmental Engineering， North Dakota State University， Fargo， North Dakota 58108
- 作者简介：
  
  [ "夏文杰，男，1988年生. 美国北达科他州立大学教授，博士生以及博士后导师，2011年于美国凯斯西储大学本科毕业，2016年于美国西北大学博士毕业. 2016~2018年于国家标准与技术研究院(NIST)的材料基因组计划(MGI)从事博士后研究. 研究工作得到美国国家自然科学基金委(NSF)、美国海军(ONR)、美国陆军(ARO)、美国国家航空航天局(NASA)多个州和地方机构资助. 曾获得多项荣誉和奖项，包括NDSU工程学院早期职业研究卓越奖(2022年)、NIST-MML技术卓越奖(2019年)、MGI奖学金、APS Padden候选人奖(2016年)以及国家优秀留学生奖学金(2015年). 在高分子科学和计算材料等领域发表超过65篇文章以及书籍著作，包括《Science Advances》《Matter》《Advanced Functional Materials》《ACS Nano》《Nano Letters》等. 主要研究方向为通过计算力学和多尺度模拟设计复杂材料." ]
  [ "徐文生，男，1984年生. 研究员，博士生导师，获国家自然科学基金优秀青年科学基金资助. 2007年毕业于天津大学材料科学与工程学院，获工学学士学位；2012年毕业于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，获理学博士学位；2013~2018年先后在美国芝加哥大学和美国橡树岭国家实验室从事博士后研究；2019年至今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任研究员. 主要从事非晶高分子动力学的理论研究，利用统计理论、计算机模拟和机器学习等方法研究高分子玻璃化的物理机制." ]
- 基金信息：
  
  国家自然科学基金(基金号 ┣22222307;21973089);22203087┫;北达科他州立大学工程学院资助项目
- DOI：10.11777/j.issn1000-3304.2022.22367
  中图分类号：
- 纸质出版日期：2023-04-20，
  
  网络出版日期：2023-01-03，
  
  收稿日期：2022-11-01，
  
  修回日期：2022-11-17，
扫描看全文
杨镇岳,聂文建,刘伦洋等.机器学习方法在高分子玻璃化研究中的应用,[J].高分子学报,2023,54(04):432-450.

Yang Zhen-yue,Nie Wen-jian,Liu Lun-yang,et al.Applications of Machine Learning Methods in the Studies of Polymer Glass Formation[J].ACTA POLYMERICA SINICA,2023,54(04):432-450.
杨镇岳,聂文建,刘伦洋等.机器学习方法在高分子玻璃化研究中的应用,[J].高分子学报,2023,54(04):432-450. DOI： 10.11777/j.issn1000-3304.2022.22367.

Yang Zhen-yue,Nie Wen-jian,Liu Lun-yang,et al.Applications of Machine Learning Methods in the Studies of Polymer Glass Formation[J].ACTA POLYMERICA SINICA,2023,54(04):432-450. DOI： 10.11777/j.issn1000-3304.2022.22367.

摘要

高分子玻璃的物理性质与其结构和动力学密切相关. 揭示高分子玻璃化的微观物理图像对高分子玻璃材料的结构调控和分子设计至关重要. 然而，高分子的长链结构和复杂单体结构特征致使目前仍然缺乏普适的理论或者模型来定量解释高分子玻璃化的物理机制. 因此，亟需发展更为先进的研究方法从而更深入地理解高分子玻璃化. 近年来，国内外学者利用基于数据驱动的信息学方法(例如机器学习)对高分子玻璃化开展了研究，并取得了丰富成果. 本综述首先介绍了常用的高分子信息学数据库和机器学习算法. 之后，从高分子玻璃化转变温度的预测、新型高分子玻璃材料的研发、过冷液体的结构-动力学关系和玻璃体系相变的确定四个方面总结和评述了机器学习应用在玻璃化研究中的代表性进展. 最后，探讨了机器学习方法在高分子玻璃化研究中面临的主要挑战，并对玻璃信息学这一领域的发展进行了展望.

Abstract

The physical properties of polymer glasses are closely related to their structure and dynamics. A deep understanding of the microscopic physical mechanism of polymer glass formation is crucial for the structural control and molecular design of polymer glasses. However

various molecular characteristics associated with complex segmental structures and chain topology of polymers impose significant challenges for the development of a fully predictive theory to describe their glass formation in a quantitative way. Hence

it is highly desired to develop more advanced approaches to better understand and predict polymer glass formation. In recent years

growing efforts have been made to shed new lights on polymer glass formation based on the data-driven informatics approaches

such as machine learning

and important progresses have been made towards this direction. The present review first introduces common polymer databases and machine learning algorithms

followed by a summary and review of the representative progresses on the applications of machine learning methods in the studies of polymer glass formation. In particular

the focus is placed on the prediction of the glass transition temperature

the research and development of novel glassy polymer materials

the investigation of structure-dynamics relationships of glass-forming liquids

and the determination of phase transitions of glasses. Finally

the present review discusses challenges and opportunities in the applications of machine learning methods to polymer glass formation and provides a perspective on glass informatics.

关键词

信息学高分子玻璃化机器学习理论模拟

Keywords

InformaticsPolymer glass formationMachine learningTheory and simulation

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