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Data-Driven Design of Polymer-Based Biomaterials: High-throughput Simulation, Experimentation, and Machine Learning.
Patel, Roshan A; Webb, Michael A.
Affiliation
  • Patel RA; Department of Chemical and Biological Engineering, Princeton University, Princeton, New Jersey 08540, United States.
  • Webb MA; Department of Chemical and Biological Engineering, Princeton University, Princeton, New Jersey 08540, United States.
ACS Appl Bio Mater ; 7(2): 510-527, 2024 Feb 19.
Article in En | MEDLINE | ID: mdl-36701125
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.
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Polymers / Biocompatible Materials Type of study: Prognostic_studies Language: En Journal: ACS Appl Bio Mater Year: 2024 Document type: Article

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Polymers / Biocompatible Materials Type of study: Prognostic_studies Language: En Journal: ACS Appl Bio Mater Year: 2024 Document type: Article