期刊
CHEMISTRY LETTERS
卷 47, 期 3, 页码 284-287出版社
CHEMICAL SOC JAPAN
DOI: 10.1246/cl.171130
关键词
Prediction of reaction yields; Machine learning|; Catalyst informatics
资金
- New Energy and Industrial Technology Development Organization (NEDO) [P16010]
- Grants-in-Aid for Scientific Research [25330283, 16H01535] Funding Source: KAKEN
Prediction of reaction yields by machine learning approach is demonstrated in tungsten-catalyzed epoxidation of alkenes. The various electronic and vibrational parameters of the phosphonic acids are collected by DFT simulation, and chosen by LASSO as the essential parameters for prediction of the reaction yields. With the trained model, we can predict yields of the reaction with unverified phosphonic acids with an error of 26%.
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