简化有机的有机的自动发现
Annabel R Basford1, Steven K Bennett1, Muye Xiao1
1Department of Chemistry, Molecular Sciences Research Hub, Imperial College London White City Campus, 82 Wood Lane W12 0BZ UK k.jelfs@imperial.ac.uk r.greenaway@imperial.ac.uk.
Chemical science
|May 3, 2024
概括
这项研究引入了一种混合工作流,将自动化实验和计算建模结合起来,以加快发现多孔有机的速度. 这种数据驱动的方法显著加速了新有机材料的识别.
科学领域:
- 材料科学 材料科学 材料科学
- 有机化学 有机化学
- 计算化学计算化学
背景情况:
- 动态共价化学 (DCC) 允许创建复杂的有机组件.
- DCC反应的可逆性质使结果的预测变得复杂,阻碍了新材料的发现.
- 传统的实验方法发现新的有机材料是缓慢的,经常依赖于机会.
研究的目的:
- 开发和实施精简的混合工作流程,以加速发现多孔有机.
- 展示如何自动化实验,数据分析和计算建模可以识别具有特定属性的有机子.
- 建立一种可访问的,数据驱动的材料发现方法.
主要方法:
- 采用了集成自动化高通量实验,自动化数据分析和计算建模的混合工作流.
- 55个化物和胺基前体的图书馆被策划用于选366种胺基凝结反应.
- 计算模型被用来预测1464个假设的有机子结果.
- 使用名为"cagey"的Python管道进行了自动化数据分析,集成质谱,度测量和1HNMR光谱.
主要成果:
- 从高通量屏幕实验识别了225个多孔的有机子.
- 54个子被发现是干净形成的,具有单一的拓,通过度和1H NMR得到证实.
- "cagey"的Python管道将数据分析时间缩短了350倍以上.
- 工作流允许大规模的数据策划用于材料发现.
结论:
- 结合自动化合成,表征和分析工作流程,显著加快了发现多孔有机的速度.
- 这种数据驱动的方法提高了发现新有机材料的效率和可访问性.
- 提出的方法方便通过系统的选和分析来识别具有所需特性的有机.
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