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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

This summary is machine-generated.

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.

Keywords:

artificial intelligence biomaterials machine learning

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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.