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Biomaterials by design: Harnessing data for future development
Kun Xue1, FuKe Wang1, Ady Suwardi1
1Institute of Materials Research and Engineering, A∗STAR (Agency for Science, Technology and Research), 2 Fusionopolis Way, Innovis, #08-03, Singapore, 138634, Singapore.
Materials Today. Bio
|December 8, 2021
Summary
Machine learning accelerates biomaterial discovery and design by analyzing vast datasets, overcoming traditional lengthy development cycles for polymeric, metallic, ceramic, and nanomaterials. This data-driven approach enhances innovation and commercialization potential.
Area of Science:
- Biomaterials Science
- Materials Engineering
- Computational Science
Background:
- Biomaterials research aims to create materials with specific biological interactions.
- Commercialization of biomaterials faces challenges due to long development timelines and high failure rates.
- Current empirical methods are being augmented by data-intensive strategies.
Purpose of the Study:
- To review the application of machine learning in biomaterial discovery and design.
- To explore how machine learning can accelerate the development pipeline.
- To discuss the integration of machine learning with emerging technologies like 3D printing.
Main Methods:
- Review of recent literature on machine learning applications in biomaterials.
- Analysis of machine learning's role in designing various material types (polymeric, metallic, ceramics, nanomaterials).
- Examination of machine learning's interface with 3D printing for biomaterial fabrication.
Main Results:
- Machine learning (ML) offers significant potential to expedite the discovery and design of novel biomaterials.
- ML techniques can process large datasets, enabling a shift from empirical to data-driven development.
- Integration of ML with 3D printing presents new avenues for customized biomaterial solutions.
Conclusions:
- Machine learning is a powerful tool for accelerating biomaterial innovation across diverse material classes.
- A data-centric approach, powered by ML, can significantly reduce development time and improve success rates.
- Further integration of ML is crucial to realize the full potential of advanced biomaterials and manufacturing.
