Comment on "Predicting reaction performance in C-N cross-coupling using machine learning"

doi:10.1126/science.aat8603

GSTDTAP > 地球科学

DOI	10.1126/science.aat8603
	Comment on "Predicting reaction performance in C-N cross-coupling using machine learning"
	Chuang, Kangway V.; Keiser, Michael J.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	Ahneman et al. (Reports, 13 April 2018) applied machine learning models to predict C-N cross-coupling reaction yields. The models use atomic, electronic, and vibrational descriptors as input features. However, the experimental design is insufficient to distinguish models trained on chemical features from those trained solely on random-valued features in retrospective and prospective test scenarios, thus failing classical controls in machine learning.
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:000450488500001
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/200085
专题	地球科学资源环境科学气候变化
作者单位	1.Univ Calif San Francisco, Inst Neurodegenerat Dis, Dept Bioengn & Therapeut Sci, Dept Pharmaceut Chem, San Francisco, CA 94143 USA; 2.Univ Calif San Francisco, Bakar Computat Hlth Sci Inst, San Francisco, CA 94143 USA
推荐引用方式 GB/T 7714	Chuang, Kangway V.,Keiser, Michael J.. Comment on "Predicting reaction performance in C-N cross-coupling using machine learning"[J]. SCIENCE,2018,362(6416).
APA	Chuang, Kangway V.,&Keiser, Michael J..(2018).Comment on "Predicting reaction performance in C-N cross-coupling using machine learning".SCIENCE,362(6416).
MLA	Chuang, Kangway V.,et al."Comment on "Predicting reaction performance in C-N cross-coupling using machine learning"".SCIENCE 362.6416(2018).