结合贝叶斯优化和自动化,同时优化反应条件和路线
Oliver Schilter1,2, Daniel Pacheco Gutierrez3, Linnea M Folkmann3
1IBM Research Europe Säumerstrasse 4 8803 Rüschlikon Switzerland oli@zurich.ibm.com.
Chemical science
|May 24, 2024
概括
这项研究展示了一个人工智能驱动的平台,用于优化化学反应. 它仅在23次实验中实现了四种基质的80%以上的转化,展示了高效的,数据驱动的化学工艺开发.
科学领域:
- 化学中的人工智能.
- 化学过程优化 化学过程优化
- 反应工程的反应工程.
背景情况:
- 最佳的反应条件对于高产量,减少副产品和可持续化学是必不可少的.
- 传统的试错方法耗时且效率低下.
- 人工智能 (AI) 为化学优化提供了数据驱动的替代方案.
研究的目的:
- 展示一个用于自动化化学反应优化的集成平台.
- 为了同时优化多个基板和反应路径.
- 为了证明AI在探索化学反应空间方面的效率.
主要方法:
- 利用一个集成的自动化和贝叶斯优化平台.
- 对四种不同的终端基和两个反应路径进行了同时优化.
- 通过有限数量的实验探索了组合空间的一小部分.
主要成果:
- 在所有四个测试的基板上都实现了超过80%的转化率.
- 在23个实验中完成了优化,约占组合空间的0.2%.
- 确定了各种反应参数对结果的影响.
结论:
- 集成的人工智能平台显著加快了反应条件优化.
- 证明了更高效和可持续的化学过程的潜力.
- 突出了现代合成化学中数据驱动方法的力量.
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