GSTDTAP  > 地球科学
DOI10.1126/science.aat8763
Response to Comment on "Predicting reaction performance in C-N cross-coupling using machine learning"
Estrada, Jesus G.1; Ahneman, Derek T.1; Sheridan, Robert P.2; Dreher, Spencer D.3; Doyle, Abigail G.1
2018-11-16
发表期刊SCIENCE
ISSN0036-8075
EISSN1095-9203
出版年2018
卷号362期号:6416
文章类型Editorial Material
语种英语
国家USA
英文摘要

We demonstrate that the chemical-feature model described in our original paper is distinguishable from the nongeneralizable models introduced by Chuang and Keiser. Furthermore, the chemical-feature model significantly outperforms these models in out-of-sample predictions, justifying the use of chemical featurization from which machine learning models can extract meaningful patterns in the dataset, as originally described.


领域地球科学 ; 气候变化 ; 资源环境
收录类别SCI-E ; SSCI
WOS记录号WOS:000450488500002
WOS类目Multidisciplinary Sciences
WOS研究方向Science & Technology - Other Topics
引用统计
文献类型期刊论文
条目标识符http://119.78.100.173/C666/handle/2XK7JSWQ/200084
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 & Co Inc, Modeling & Informat, Kenilworth, NJ 07033 USA;
3.Merck & Co Inc, Chem Capabil & Screening, Kenilworth, NJ 07033 USA
推荐引用方式
GB/T 7714
Estrada, Jesus G.,Ahneman, Derek T.,Sheridan, Robert P.,et al. Response to Comment on "Predicting reaction performance in C-N cross-coupling using machine learning"[J]. SCIENCE,2018,362(6416).
APA Estrada, Jesus G.,Ahneman, Derek T.,Sheridan, Robert P.,Dreher, Spencer D.,&Doyle, Abigail G..(2018).Response to Comment on "Predicting reaction performance in C-N cross-coupling using machine learning".SCIENCE,362(6416).
MLA Estrada, Jesus G.,et al."Response to Comment on "Predicting reaction performance in C-N cross-coupling using machine learning"".SCIENCE 362.6416(2018).
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