基于catboost的耦合反应智能预测与反应条件分析框架

IF 2.9 2区化学 Q2 CHEMISTRY, MULTIDISCIPLINARY

Match-Communications in Mathematical and in Computer Chemistry Pub Date : 2023-01-01 DOI:10.46793/match.90-1.053w

Hengzhe Wang, Lichao Peng, Li Chang, Zixin Li, Yanhui Guo, Qian Li, Xiaohui Yang

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引用次数: 0

摘要

机器学习在预测化学反应性能方面越来越受欢迎。本研究旨在应用CatBoost算法构建有机化学反应产率的智能预测系统。进行了参数分析、收敛性分析、预测精度分析和泛化分析。然后，通过特征重要性和SHAP挖掘反应条件与产率之间的内在关系。结果表明，该方法具有作为辅助化学反应体系优化的高精度工具的潜力。

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CatBoost-Based Framework for Intelligent Prediction and Reaction Condition Analysis of Coupling Reaction

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.

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来源期刊

Match-Communications in Mathematical and in Computer Chemistry 化学-化学综合

CiteScore

4.40

自引率

26.90%

发文量

审稿时长

2 months

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.