GSTDTAP  > 地球科学
DOI10.1126/science.aar5169
Predicting reaction performance in C-N cross-coupling using machine learning
Ahneman, Derek T.1; Estrada, Jesus G.1; Lin, Shishi2; Dreher, Spencer D.2; Doyle, Abigail G.1
2018-04-13
发表期刊SCIENCE
ISSN0036-8075
EISSN1095-9203
出版年2018
卷号360期号:6385页码:186-190
文章类型Article
语种英语
国家USA
英文摘要

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.


领域地球科学 ; 气候变化 ; 资源环境
收录类别SCI-E
WOS记录号WOS:000429805400042
WOS关键词CATALYSIS ; REGRESSION ; DISCOVERY ; TOOL
WOS类目Multidisciplinary Sciences
WOS研究方向Science & Technology - Other Topics
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文献类型期刊论文
条目标识符http://119.78.100.173/C666/handle/2XK7JSWQ/198404
专题地球科学
资源环境科学
气候变化
作者单位1.Princeton Univ, Dept Chem, Princeton, NJ 08544 USA;
2.Merck Sharp & Dohme Corp, Chem Capabil & Screening, Kenilworth, NJ 07033 USA
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GB/T 7714
Ahneman, Derek T.,Estrada, Jesus G.,Lin, Shishi,et al. Predicting reaction performance in C-N cross-coupling using machine learning[J]. SCIENCE,2018,360(6385):186-190.
APA Ahneman, Derek T.,Estrada, Jesus G.,Lin, Shishi,Dreher, Spencer D.,&Doyle, Abigail G..(2018).Predicting reaction performance in C-N cross-coupling using machine learning.SCIENCE,360(6385),186-190.
MLA Ahneman, Derek T.,et al."Predicting reaction performance in C-N cross-coupling using machine learning".SCIENCE 360.6385(2018):186-190.
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