用硫化去氧化:通过机器学习导航反应空间
Matthew K Nielsen1, Derek T Ahneman1, Orestes Riera1
1Department of Chemistry , Princeton University , Princeton , New Jersey 08544 , United States.
Journal of the American Chemical Society
|March 28, 2018
概括
硫化物通过优化试剂和基结构有效地化醇. 机器学习可以准确地预测新的酒精基质的高产量条件,从而推进合成化学.
科学领域:
- 有机化学
- 合成化学
- 计算化学
背景情况:
- 硫化物是用于化酒精的多功能试剂.
- 优化反应条件对于各种酒精类别的有效化至关重要.
- 预测新基质的最佳条件仍然是合成化学的一个挑战.
研究的目的:
- 开发一种使用硫烯化物化各种酒精的有效方法.
- 利用机器学习预测最佳反应条件.
- 绘制硫化中介酒精化的复杂反应场景.
主要方法:
- 硫化物试剂结构的微调.
- 为化反应优化基结构.
- 应用机器学习模型来预测反应结果.
- 对各种酒精基质进行选.
主要成果:
- 证明了各种酒精类别的高效化.
- 确定了影响产量的试剂和基的主要结构特征.
- 开发精确的机器学习模型来预测高收益条件.
- 成功预测了以前未经测试的酒精基质的最佳条件.
结论:
- 硫化物化学,当微调时,提供一个高效的醇路径.
- 机器学习是指导复杂反应的强大工具.
- 这种方法可以准确地预测最佳条件,加速发现新的化方法.
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