New Opportunity: Machine Learning for Polymer Materials Design and Discovery. Issue 5 (12th February 2022)

• Record Type: Journal Article
• Authors: Xu, Pengcheng; Chen, Huimin; Li, Minjie; Lu, Wencong
• Is Part Of: Advanced theory and simulations. Volume 5:Issue 5(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-02-12
• Subjects: machine learning -- materials design and discovery -- polymer -- support vector machine -- transfer learning; Science -- Simulation methods -- Periodicals; Science -- Methodology -- Periodicals; Engineering -- Simulation methods -- Periodicals; Engineering -- Methodology -- Periodicals; 507.21
• DOI: 10.1002/adts.202100565
• Languages: English
• ISSNs: 2513-0390

Abstract

Abstract: Under the guidance of the material genome initiative (MGI), the use of data‐driven methods to discover new materials has become an innovation of materials science. The polymer materials have been one of the most important parts in materials science for the excellent physical and chemical properties as well as corresponding complex structures. Machine learning, as the core of data‐driven methods, has taken an important place in polymer materials design and discovery. In this review, the authors have introduced the applications of machine learning in the design and discovery of polymer materials. The development tendency of published papers about machine learning in polymer materials, the commonly used algorithms, the polymer descriptors, the workflow of machine learning in polymer materials, and recent progresses of machine learning in materials are summarized. Then, the detail of how to use machine learning to assist design and discovery of polymer materials is fully discussed combined with two cases. Finally, the opportunities and challenges on the future development prospects of machine learning in the field of polymer materials are proposed. Abstract : The review has retrospected the latest applications of machine learning in the design and discovery of polymer materials, including the development of published papers, the algorithms, the polymer descriptors, the workflow, and recent progress and case study. The opportunities and challenges of machine learning in polymer materials have also been proposed in this review.