扭捏
催化作用
随机森林
计算机科学
钯
集合(抽象数据类型)
化学
简单(哲学)
吞吐量
机器学习
组合化学
人工智能
计算化学
有机化学
操作系统
哲学
认识论
电信
程序设计语言
无线
作者
Derek T. Ahneman,Jesús G. Estrada,Shishi Lin,Spencer D. Dreher,Abigail G. Doyle
出处
期刊:Science
[American Association for the Advancement of Science (AAAS)]
日期:2018-04-13
卷期号:360 (6385): 186-190
被引量:628
标识
DOI:10.1126/science.aar5169
摘要
Machine learning methods are becoming integral to scientific inquiry in numerous disciplines. We demonstrated that machine learning can be used to predict the performance of a synthetic reaction in multidimensional chemical space using data obtained via high-throughput experimentation. We created scripts to compute and extract atomic, molecular, and vibrational descriptors for the components of a palladium-catalyzed Buchwald-Hartwig cross-coupling of aryl halides with 4-methylaniline in the presence of various potentially inhibitory additives. Using these descriptors as inputs and reaction yield as output, we showed that a random forest algorithm provides significantly improved predictive performance over linear regression analysis. The random forest model was also successfully applied to sparse training sets and out-of-sample prediction, suggesting its value in facilitating adoption of synthetic methodology.
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