Hengzhe Wang, Lichao Peng, Li Chang, Zixin Li, Yanhui Guo, Qian Li, Xiaohui Yang
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引用次数: 0
Abstract
Machine learning is increasingly popular in predicting chemical reaction performance. This study aims to apply the CatBoost algorithm to build an intelligent prediction system for organic chemical reaction yields. The parameter analysis, convergence analysis, prediction accuracy analysis and generalization analysis are carried out. Then, the internal relationship between reaction conditions and yield is excavated through feature importance and SHAP. The results show that the proposed method has the potential as a high-precision tool to assist the optimization of chemical reaction system.
期刊介绍：
MATCH Communications in Mathematical and in Computer Chemistry publishes papers of original research as well as reviews on chemically important mathematical results and non-routine applications of mathematical techniques to chemical problems. A paper acceptable for publication must contain non-trivial mathematics or communicate non-routine computer-based procedures AND have a clear connection to chemistry. Papers are published without any processing or publication charge.
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