Data-Driven Design of Polymer-Based Biomaterials: High-throughput Simulation, Experimentation, and Machine Learning

Roshan A. Patel, Michael A. Webb

Research output: Contribution to journalReview articlepeer-review

7 Scopus citations

Abstract

Polymers, with the capacity to tunably alter properties and response based on manipulation of their chemical characteristics, are attractive components in biomaterials. Nevertheless, their potential as functional materials is also inhibited by their complexity, which complicates rational or brute-force design and realization. In recent years, machine learning has emerged as a useful tool for facilitating materials design via efficient modeling of structure−property relationships in the chemical domain of interest. In this Spotlight, we discuss the emergence of data-driven design of polymers that can be deployed in biomaterials with particular emphasis on complex copolymer systems. We outline recent developments, as well as our own contributions and takeaways, related to high-throughput data generation for polymer systems, methods for surrogate modeling by machine learning, and paradigms for property optimization and design. Throughout this discussion, we highlight key aspects of successful strategies and other considerations that will be relevant to the future design of polymer-based biomaterials with target properties.

Original languageEnglish (US)
Pages (from-to)510-527
Number of pages18
JournalACS Applied Bio Materials
Volume7
Issue number2
DOIs
StatePublished - Feb 19 2024

All Science Journal Classification (ASJC) codes

  • General Chemistry
  • Biochemistry, medical
  • Biomedical Engineering
  • Biomaterials

Keywords

  • active learning
  • copolymer featurization
  • high-throughput characterization
  • polymer automation
  • polymer biomaterials
  • polymer design
  • protein−polymer interactions
  • structure−property surrogate modeling

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