数据驱动的反应性预测和对C (sp2) - (hetero) 原子键合的添加选择在适应性动态均催化中
Li-Yang Fan1, Xue-Tao Li1, Xi-Xi Luo1
1Institute of Functional Material Chemistry, Faculty of Chemistry, Northeast Normal University, Changchun, 130024, P. R. China.
Chemistry (Weinheim an der Bergstrasse, Germany)
|April 22, 2025
概括
研究人员开发了一种适应性动态同质催化 (AD-HoC) 平台,用于高效的C(sp2) (hetero) 原子键形成. 这种基于机器学习的方法预测了基质的反应性,并优化了反应条件,提高了合成精度.
科学领域:
- 有机化学 有机化学
- 催化剂是一种催化剂.
- 计算化学计算化学
背景情况:
- 在可见光下的过渡金属催化对于C (sp2) (hetero) 原子键的形成至关重要.
- 对于这些重要的反应,通常需要精确优化反应参数.
- 现有的方法需要对催化剂,连接物和进行广泛的调整.
研究的目的:
- 为光催化交叉合反应引入适应性动态同质催化 (AD-HoC) 平台.
- 通过改变添加剂和基质,消除了优化催化剂,连接体和基的需要.
- 在AD-HoC系统中利用机器学习来预测基质反应性和添加剂选择.
主要方法:
- 高通量量子力学计算与化学信息学的整合.
- 机器学习的应用用于评估基质组合和添加物类别.
- 开发使用SMILES (简化分子输入线输入系统) 表示的端到端预测工具.
主要成果:
- 通过修改添加剂和基质,AD-HoC系统可以实现C(sp2) (hetero) 原子键合.
- 电友的电子特征和核友的几何特征被确定为关键的反应性调节剂.
- 基于数据的分析显示了在AD-HoC框架内可预测的反应条件.
结论:
- 该研究提出了使用计算统计和机器学习来预测反应活性和反应条件的协作方法.
- 这种方法提高了合成过程的精度和效率.
- AD-HoC平台为光催化交叉合反应提供了一种强大且高产率的方法.
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