Biomaterials by design: Harnessing data for future development. (September 2021)

• Record Type: Journal Article
• Authors: Xue, Kun; Wang, FuKe; Suwardi, Ady; Han, Ming-Yong; Teo, Peili; Wang, Pei; Wang, Shijie; Ye, Enyi; Li, Zibiao; Loh, Xian Jun
• Is Part Of: Materials today bio. Volume 12(2021)
• Publication Date: 2021-09
• Subjects: Materials science -- Periodicals; Biomedical engineering -- Periodicals; Biomedical materials -- Periodicals; 620.1
• DOI: 10.1016/j.mtbio.2021.100165
• Languages: English
• ISSNs: 2590-0064

Abstract

Abstract: Biomaterials is an interdisciplinary field of research to achieve desired biological responses from new materials, regardless of material type. There have been many exciting innovations in this discipline, but commercialization suffers from a lengthy discovery to product pipeline, with many failures along the way. Success can be greatly accelerated by harnessing machine learning techniques to comb through large amounts of data. There are many potential benefits of moving from an unstructured empirical approach to a development strategy that is entrenched in data. Here, we discuss the recent work on the use of machine learning in the discovery and design of biomaterials, including new polymeric, metallic, ceramics, and nanomaterials, and how machine learning can interface with emerging use cases of 3D printing. We discuss the steps for closer integration of machine learning to make this exciting possibility a reality. Graphical abstract: Image 1