Machine Learning-Guided Prediction of Hydroformylation.

IF 2.3 3区化学 Q3 CHEMISTRY, PHYSICAL

Chemphyschem Pub Date : 2024-10-29 DOI:10.1002/cphc.202400773

Haonan Shi, Chaoren Shen, Zheng Huang, Kaiwu Dong

引用次数: 0

Abstract

A holistic model for predicting yield and linear selectivity for the hydroformylation of 1-octene was developed by machine learning using the experimental data collected from literatures. Physical organic chemistry (POC) parameter-based descriptors were adopted to represent pre-catalyst molecular features. Machine learning models trained respectively by Random Forests (RF) and Extreme Gradient Boost (XGBoost) algorithm showed remarkable performance on predicting linear selectivity. The method can also comprehensively map the correlation between reaction conditions and the results. The accuracy of the prediction results was verified by experimental data.

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机器学习指导下的加氢甲酰化预测。

利用从文献中收集的实验数据，通过机器学习建立了一个整体模型，用于预测 1-辛烯加氢甲酰化的产率和线性选择性。采用基于物理有机化学（POC）参数的描述符来表示前催化剂分子特征。分别采用随机森林（RF）和极端梯度提升（XGBoost）算法训练的机器学习模型在预测线性选择性方面表现出了显著的性能。该方法还能全面反映反应条件与结果之间的相关性。实验数据验证了预测结果的准确性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Chemphyschem 化学-物理：原子、分子和化学物理

CiteScore

4.60

自引率

3.40%

发文量

425

审稿时长

1.1 months

期刊介绍： ChemPhysChem is one of the leading chemistry/physics interdisciplinary journals (ISI Impact Factor 2018: 3.077) for physical chemistry and chemical physics. It is published on behalf of Chemistry Europe, an association of 16 European chemical societies. ChemPhysChem is an international source for important primary and critical secondary information across the whole field of physical chemistry and chemical physics. It integrates this wide and flourishing field ranging from Solid State and Soft-Matter Research, Electro- and Photochemistry, Femtochemistry and Nanotechnology, Complex Systems, Single-Molecule Research, Clusters and Colloids, Catalysis and Surface Science, Biophysics and Physical Biochemistry, Atmospheric and Environmental Chemistry, and many more topics. ChemPhysChem is peer-reviewed.