协聚合物序列调节通过机器学习的反应率指纹实现
Zexi Zhang1, Chengda Zhou1, Yufei Chen1
1Department of Macromolecular Science, State Key Laboratory of Molecular Engineering of Polymers, Research Center of AI for Polymer Science, Fudan University, Shanghai, 200433, China.
Angewandte Chemie (International ed. in English)
|October 28, 2025
概括
本研究介绍了一种机器学习平台,使用反应率指纹 (rFP) 来高效地确定共聚合物的反应率. 这种方法使得按需的聚合物序列可根据先进的材料特性进行定制.
科学领域:
- 聚合物化学 聚合物化学
- 材料科学 材料科学 材料科学
- 计算化学计算化学
背景情况:
- 序列控制对于在先进应用中调整聚合物特性至关重要.
- 反应率是分析和调节聚合物序列的关键参数.
- 确定反应率的传统方法是低效的,并且仅限于二进制系统.
研究的目的:
- 开发一个机器学习平台,以高效地确定反应率.
- 为了使二进制和三进制共聚合物的反应率分析.
- 为了促进按需的聚合物序列定制和物质工程.
主要方法:
- 开发一个机器学习平台,利用新的"反应率指纹" (rFPs).
- 在数以百万计的RFP中训练深度学习模型,以确定高效率.
- 应用到二进制和三进制共聚合物与稀疏的实验数据.
主要成果:
- 从最小的数据中确定反应率的毫秒级别.
- 在各种条件 (温度,溶剂) 中表现出多功能性.
- 成功的RFP指导反应设计,用于按需量身定制序列.
- 确定二进制和三进制的亚热热态共聚合物.
结论:
- 开发的机器学习平台为确定反应率提供了一个高效的策略.
- 该方法为测序复杂的聚合物链提供了一个可通用的框架.
- 这便于精确控制高分子特性,用于先进的应用.
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