通过高通量计算机控制的聚合学转移学习全分子重量分布
Jin Da Tan1,2, Balamurugan Ramalingam1,3, Swee Liang Wong1,4
1Institute of Materials Research & Engineering, Agency for Science Technology and Research, 2 Fusionopolis Way, 138634 Singapore, Singapore.
Journal of chemical information and modeling
|July 11, 2023
概括
机器学习 (ML) 和高通量实验 (HTE) 预测了聚合物分子重量分布 (MWD) 和动力学. 这种方法可以通过准确预测MWD,包括斜率和形状来精确控制聚合物特性.
科学领域:
- 聚合物化学 聚合物化学
- 化学工程是化学工程的重要组成部分.
- 数据科学数据科学数据科学
背景情况:
- 聚合物的物理性质受到其分子重量分布 (MWD) 的倾斜和形状的显著影响.
- 传统的统计总结指标对整个MWD提供了有限的洞察力.
- 在没有信息丢失的情况下预测整个MWD对于材料设计至关重要.
研究的目的:
- 展示一个高通量实验 (HTE) 平台,用于预测聚合物分子重量分布 (MWD).
- 将机器学习 (ML) 模型与HTE集成,以进行全面的MWD预测.
- 探索转移学习的应用,以便在批量聚合过程中高效地预测MWD.
主要方法:
- 在8个可变条件下开发了一种计算机控制的HTE系统,用于在8个可变条件下并行对 styrene 的自由基聚合.
- 使用直线拉曼光谱进行实时单体转换和离线尺寸排除色谱 (SEC) 进行MWD分析.
- 采用ML前模型来预测单体转换和MWD,并将SHAP分析纳入解释性.
主要成果:
- ML模型准确地预测了单体转化,在不同的实验条件下捕获了不同的聚合动力学.
- 整个MWD,包括斜和形状,都成功预测了,SHAP分析显示了对反应参数的依赖性.
- 转移学习允许使用最小的额外数据点准确预测批量聚合MWDs.
结论:
- HTE和ML的协同组合为聚合结果提供了高预测准确度.
- 这种综合方法克服了传统MWD分析的局限性.
- 转移学习促进了合成参数空间的有效探索,使得有针对性的聚合物设计成为可能.
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