Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1126/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 |
ISSN | 0036-8075 |
EISSN | 1095-9203 |
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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