由多目标贝叶斯优化加速的盐的连续流合成与主动学习的贝叶斯优化
John H Dunlap1,2, Jeffrey G Ethier1,2, Amelia A Putnam-Neeb1,3
1Materials and Manufacturing Directorate, Air Force Research Laboratory Wright-Patterson AFB OH 45433 USA luke.baldwin.1@us.af.mil.
Chemical science
|August 4, 2023
概括
我们使用贝叶斯优化 (EDBO+) 在连续流程中优化了丁二化合成,实现了高产量和生产率. 数据分析方法影响了模型预测,突出了准确的核磁共振 (NMR) 解释的重要性.
科学领域:
- 化学工程是化学工程的重要组成部分.
- 有机化学 有机化学
- 计算化学计算化学
背景情况:
- 连续流化学提供了对传统批处理过程的增强控制和可扩展性.
- 贝叶斯优化是多目标实验设计的强大工具.
- 准确的数据分析,特别是来自核磁共振 (NMR) 光谱,对于可靠的化学合成优化至关重要.
研究的目的:
- 实施一个人-在-循环贝叶斯优化平台 (EDBO+) 优化丁二合成.
- 在连续流条件下,同时优化反应产量和生产率 (时空产量).
- 调查不同数据分析方法对优化模型预测的影响.
主要方法:
- 使用贝叶斯优化平台 (EDBO+) 进行多目标实验设计.
- 采用连续流反应器进行合成,从而实现精确的参数控制.
- 应用nmrglue Python模块进行半自动化NMR数据分析,并与手动处理进行比较.
- 用使用两种分析方法的低场和高场NMR光谱仪的数据重新训练EDBO+模型.
主要成果:
- 成功生成了帕雷托前线,优化了反应产量和时空产量.
- 通过在竞选中期扩大反应温度范围来证明平台的多功能性.
- 展示了NMR数据分析技术对优化模型预测精度的影响.
- 扩展了连续流化学,通过聚4-乙烯二的四度化合成功能材料).
结论:
- EDBO+有效地优化了连续流中的多目标化学合成.
- 数据分析方法的选择显著影响贝叶斯优化模型的性能.
- 连续流化学与强大的数据分析相结合,为化学合成和材料开发提供了可扩展和自动化的路线.
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