重新审视反应优化在流程中的范式,以先验的计算反应智能
Pauline Bianchi1, Jean-Christophe M Monbaliu1,2
1Center for Integrated Technology and Organic Synthesis (CiTOS), MolSys Research Unit, University of Liège, B6a, Room 3/19, Allée du Six Août 13, 4000, Liège (SartTilman), Belgium.
Angewandte Chemie (International ed. in English)
|October 24, 2023
概括
计算化学和机器学习模型通过预测反应结果来加速流化学,从而实现高效的批量到流转换和新型化合物合成.
科学领域:
- 化学工程是化学工程的重要组成部分.
- 计算化学计算化学
- 机器学习 机器学习
背景情况:
- 流化学在反应规模,反应器尺寸缩小和反应时间缩短方面具有优势.
- 将已建立的批量流程转换为流动条件往往依赖于低效的试错方法.
- 实验优化的高成本阻碍了流体化学的广泛采用.
研究的目的:
- 通过整合计算化学和机器学习来开发流动反应的预测模型.
- 为了证明该模型在将批量流程转化为流动条件中的实用性.
- 展示模型在提供机械见解和合成新型化合物的能力.
主要方法:
- 开发一种结合化学原理与机器学习算法的计算模型.
- 模型的应用用于预测各种流动反应的结果.
- 使用模型进行机械分析和试剂描述器贡献.
主要成果:
- 开发的模型表现出专门用于流体化学的预测能力.
- 我们成功地将批量流程转换为流量条件.
- 一个新型化合物的库被合成在高收益率使用优化条件预测的模型.
结论:
- 计算化学和机器学习的整合为优化流体化学提供了一个强大的工具.
- 这种方法显著减少了对实验试错的需求,使流化学更容易获得和负担得起.
- 该模型促进了高效的工艺开发,机械学理解和新化学实体的发现.
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