Machine Learning‐Driven Biomaterials Evolution. Issue 1 (7th October 2021) - Suwardi, Ady; Wang, FuKe; Xue, Kun; Han, Ming‐Yong; Teo, Peili; Wang, Pei; Wang, Shijie; Liu, Ye; Ye, Enyi; Li, Zibiao; Loh, Xian Jun
Abstract: Biomaterials is an exciting and dynamic field, which uses a collection of diverse materials to achieve desired biological responses. While there is constant evolution and innovation in materials with time, biomaterials research has been hampered by the relatively long development period required. In recent years, driven by the need to accelerate materials development, the applications of machine learning in materials science has progressed in leaps and bounds. The combination of machine learning with high‐throughput theoretical predictions and high‐throughput experiments (HTE) has shifted the traditional Edisonian (trial and error) paradigm to a data‐driven paradigm. In this review, each type of biomaterial and their key properties and use cases are systematically discussed, followed by how machine learning can be applied in the development and design process. The discussions are classified according to various types of materials used including polymers, metals, ceramics, and nanomaterials, and implants using additive manufacturing. Last, the current gaps and potential of machine learning to further aid biomaterials discovery and application are also discussed. Abstract : The advancement of machine learning (ML) in materials science has progressed in leaps and bounds and has made a big impact into biomaterials research, ranging from discovery of bioactive chemical moieties, screening and optimization of material properties, to developing materials that interface better with biological systems. There is still untapped potential to integrate with ML for the next frontier in biomaterials.