Perspectives on development of biomedical polymer materials in artificial intelligence age

被引:10
作者
Xie, Shijin [1 ,2 ]
机构
[1] Univ Melbourne, Melbourne, Vic, Australia
[2] Univ Melbourne, Grattan St, Melbourne, Vic 3010, Australia
关键词
Biomedical polymer; machine learning; data driven algorithm; organic molecule; material database; COMPOSITE-MATERIALS; MATERIALS DESIGN; NEURAL-NETWORKS; DISCOVERY; MODEL; OPPORTUNITIES; OPTIMIZATION; FINGERPRINT; TEMPERATURE; PARAMETERS;
D O I
10.1177/08853282231151822
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
Polymer materials are widely used in biomedicine, chemistry and material science, whose traditional preparations are mainly based on experience, intuition and conceptual insight, having been applied to the development of many new materials, but facing great challenges due to the vast design space for biomedical polymers. So far, the best way to solve these problems is to accelerate material design through artificial intelligence, especially machine learning. Herein, this paper will introduce several successful cases, and analyze the latest progress of machine learning in the field of biomedical polymers, then discuss the opportunities of this novel method. In particular, this paper summarizes the material database, open-source determination tools, molecular generation methods and machine learning models that have been used for biopolymer synthesis and property prediction. Overall, machine learning could be more effectively deployed on the material design of biomedical polymers, and it is expected to become an extensive driving force to meet the huge demand for customized designs.
引用
收藏
页码:1355 / 1375
页数:21
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