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Machine Learning-Driven Biomaterials Evolution
Ady Suwardi1, FuKe Wang1, Kun Xue1
1Institute of Materials Research and Engineering, A*STAR (Agency for Science, Technology and Research), 2 Fusionopolis Way, Innovis, #08-03, Singapore, 138634, Singapore.
Advanced Materials (Deerfield Beach, Fla.)
|October 7, 2021
Summary
Machine learning accelerates biomaterials discovery by integrating with high-throughput methods, shifting from trial-and-error to data-driven design for polymers, metals, ceramics, and nanomaterials.
Area of Science:
- Biomaterials science
- Materials science
- Computational materials science
Background:
- Biomaterials research traditionally faces lengthy development cycles.
- Innovation in biomaterials is crucial for advancing medical applications.
- The Edisonian (trial and error) approach limits rapid progress.
Purpose of the Study:
- To review the application of machine learning in biomaterials development.
- To systematically discuss various biomaterial types, properties, and use cases.
- To explore how machine learning can accelerate the design and discovery of new biomaterials.
Main Methods:
- Literature review of biomaterials and machine learning applications.
- Systematic classification of biomaterials (polymers, metals, ceramics, nanomaterials).
- Integration of machine learning with high-throughput theoretical predictions and experiments.
Main Results:
- Machine learning combined with high-throughput approaches enables a data-driven paradigm.
- The review covers diverse biomaterials, including those for additive manufacturing.
- Identifies current gaps and future potential for machine learning in biomaterials.
Conclusions:
- Machine learning significantly accelerates biomaterials discovery and development.
- Data-driven approaches are transforming the traditional biomaterials research landscape.
- Future research should focus on leveraging machine learning to overcome existing challenges in biomaterials application.
