Tier-grown expansion of Design-of-Experiments parameter spaces for synthesis of a nanometer-scale macrocycle
Tier-grown expansion of Design-of-Experiments parameter spaces for synthesis of a nanometer-scale macrocycle
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用于合成纳米级大环的实验设计参数空间的分层扩展
DOI：
10.1002/asia.202201141
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Hiroyuki Isobe
中科院分区：
文献类型：
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作者：
Misato Akiyoshi;Koki Ikemoto; Hiroyuki Isobe
通过将数据驱动的经验模型与传统的机械模型相结合，设计了一种寻找最佳合成条件的方法。在该方法中，通过机器学习补充的实验设计优化凭经验获得实验参数空间，并通过检查反应路径的机械模型来策略性地扩展。在原始 3×3×3 参数空间上生长的额外层成功地在扩展的 3×3×4 参数空间中分配最佳反应条件。该方法是专门为合成大环[n]环间亚苯基([n]CMP)而设计的，并且首次合成了n=12的最大同系物并对其进行了充分表征。晶体学和光物理分析揭示了 [12]CMP 对于材料应用的有利特征。
A method to find optimum synthetic conditions was devised by combining a data‐driven empirical model with a traditional mechanistic model. In this method, an experimental parameter space was empirically obtained by Design‐of‐Experiments optimizations with machine‐learning supplements and was strategically expanded by examination of the mechanistic model of the reaction paths. An extra tier grown on the original 3×3×3 parameter space succeeded in allocating an optimum reaction condition in the expanded 3×3×4 parameter space. The method was specifically devised for the synthesis of a macrocycle, [n]cyclo‐meta‐phenylenes ([n]CMP), and the largest congener withn=12 was synthesized and fully characterized for the first time. Crystallographic and photophysical analyses revealed favorable features of [12]CMP for the material applications.