Open AccessMachine learning in polymer informatics
Author(s) -
Sha Wuxin,
Li Yan,
Tang Shun,
Tian Jie,
Zhao Yuming,
Guo Yaqing,
Zhang Weixin,
Zhang Xinfang,
Lu Songfeng,
Cao YuanCheng,
Cheng Shijie
Publication year - 2021
Publication title -
infomat
Language(s) - English
Resource type - Journals
ISSN - 2567-3165
DOI - 10.1002/inf2.12167
Subject(s) - informatics , materials informatics , computer science , health informatics , process (computing) , data science , artificial intelligence , machine learning , engineering informatics , aerospace , engineering , health care , aerospace engineering , economic growth , electrical engineering , economics , operating system
Polymers have been widely used in energy storage, construction, medicine, aerospace, and so on. However, the complexity of chemical composition and morphology of polymers has brought challenges to their development. Thanks to the integration of machine learning algorithms and large data resources, the data‐driven methods have opened up a new road for the development of polymer science and engineering. The emerging polymer informatics attempts to accelerate the performance prediction and process optimization of new polymers by using machine learning models based on reliable data. With the gradual supplement of currently available databases, the emergence of new databases and the continuous improvement of machine learning algorithms, the research paradigm of polymer informatics will be more efficient and widely used. Based on these points, this paper reviews the development trends of machine learning assisted polymer informatics and provides a simple introduction for researchers in materials, artificial intelligence, and other fields.