探索软材料的广阔前景:高通量工作流程用户指南

IF 4.7 Q1 POLYMER SCIENCE ACS polymers Au Pub Date : 2023-12-05 DOI:10.1021/acspolymersau.3c00025
Erin C. Day, Supraja S. Chittari, Matthew P. Bogen and Abigail S. Knight*, 
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引用次数: 0

摘要

合成聚合物具有量身定制的结构和功能,具有很强的可定制性,但由于设计空间的规模和复杂性,这种多功能性给先进材料的设计带来了挑战。因此,利用组合库探索和优化聚合物特性已变得越来越普遍,这需要精心选择合成策略、表征技术和快速处理工作流程,以便从这些大型数据集中获取基本原理。在此,我们将为高分子库的策略设计和工作流程提供指导,以便高效地浏览这些高维设计空间。我们介绍了多种库大小和结构的合成方法,以及快速生成数据集的表征方法,包括可从生物工作流程中改编的工具。我们进一步强调了统计学和机器学习的相关见解,以帮助数据特征化、表征和分析。对于有兴趣利用高通量筛选设计多功能聚合物以及对软材料的结构-性能关系进行预测建模的研究人员来说,本《视角》是一本 "用户指南"。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Navigating the Expansive Landscapes of Soft Materials: A User Guide for High-Throughput Workflows

Synthetic polymers are highly customizable with tailored structures and functionality, yet this versatility generates challenges in the design of advanced materials due to the size and complexity of the design space. Thus, exploration and optimization of polymer properties using combinatorial libraries has become increasingly common, which requires careful selection of synthetic strategies, characterization techniques, and rapid processing workflows to obtain fundamental principles from these large data sets. Herein, we provide guidelines for strategic design of macromolecule libraries and workflows to efficiently navigate these high-dimensional design spaces. We describe synthetic methods for multiple library sizes and structures as well as characterization methods to rapidly generate data sets, including tools that can be adapted from biological workflows. We further highlight relevant insights from statistics and machine learning to aid in data featurization, representation, and analysis. This Perspective acts as a “user guide” for researchers interested in leveraging high-throughput screening toward the design of multifunctional polymers and predictive modeling of structure–property relationships in soft materials.

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