Department of Chemical and Biological Engineering, Princeton University, Princeton, New Jersey 08540, United States.
ACS Appl Bio Mater. 2024 Feb 19;7(2):510-527. doi: 10.1021/acsabm.2c00962. Epub 2023 Jan 26.
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.
聚合物可以通过改变其化学特性来调节其性质和响应，因此它们是生物材料中很有吸引力的组成部分。然而，由于其复杂性，它们作为功能材料的潜力也受到了限制，这使得合理或强力设计和实现变得复杂。近年来，机器学习已成为一种有用的工具，可以通过在感兴趣的化学领域中有效地建模结构-性质关系，来促进材料设计。在本特写文章中，我们讨论了聚合物的数据驱动设计的出现，这些聚合物可以应用于生物材料中，特别强调了复杂的共聚物系统。我们概述了最近的发展，以及我们自己的贡献和收获，这些贡献和收获涉及聚合物系统的高通量数据生成、机器学习的替代建模方法以及性能优化和设计的范例。在整个讨论过程中，我们强调了成功策略的关键方面以及其他考虑因素，这些因素对于具有目标性质的基于聚合物的生物材料的未来设计将是相关的。
