使用机器学习控制有机合成机器人以寻找新的反应性
Jarosław M Granda1, Liva Donina1, Vincenza Dragone1
1School of Chemistry, University of Glasgow, Glasgow, UK.
Nature
|July 20, 2018
概括
这项研究引入了一种有机合成机器人, 该系统在预测反应性方面达到80%以上的准确性,从而识别出新的反应.
科学领域:
- 有机化学
- 化学工程
- 人工智能
背景情况:
- 化学反应的发现是不可预测和耗时的.
- 目前的计算机辅助反应设计和机器学习应用在化学领域还处于芽阶段.
- 使用量子化学方法预测反应性是复杂的,即使对于简单的分子.
研究的目的:
- 开发一个用于快速化学反应探索和预测的自动化系统.
- 用机器学习来有效地导航化学反应空间.
- 为了加速新化学反应的发现.
主要方法:
- 一个有机合成机器人被开发来进行反应和分析.
- 机器学习算法用于决策,利用化学输入的二进制编码.
- 使用核磁共振 (NMR) 和红外 (IR) 光谱进行实时反应评估.
主要成果:
- 机器人比手工方法更快地进行化学反应和分析.
- 机器学习系统在分析了10%的数据后,预测了1000个反应组合的反应性,准确度高于80%.
- 该方法成功预测了已发布的数据集和指导手动验证的反应性,从而发现了四种新的反应.
结论:
- 机器学习控制的机器人系统可以快速探索化学反应空间.
- 这种方法极大地加速了新化学反应的发现.
- 自动合成和预测为传统的耗时方法提供了强大的替代方案.
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