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IF 5.5 1区化学 Q2 CHEMISTRY, MULTIDISCIPLINARY

Chinese Journal of Chemistry Pub Date : 2024-08-01 DOI:10.1002/cjoc.202490172

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引用次数: 0

摘要

建立了基于机器学习的综合键解离能（BDE）预测模型，该模型有助于理解分子的化学性质和反应活性。开发了反映分子在键同解过程中结构和物理化学特征变化的差异结构和物理化学（D-SPOC）描述符作为输入特征，从而实现了精确预测。更多详情请参见 Zhang 等人的文章（第 1967-1974 页）。

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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A machine learning-based comprehensive Bond Dissociation Energy (BDE) prediction model was established, which is useful for understanding the chemical properties and reactivities of molecules. Differential Structural and PhysicOChemical (D-SPOC) descriptors that reflected changes in molecules’ structural and physicochemical features in the process of bond homolysis were developed as input features, which enabled the precise prediction. More details are discussed in the article by Zhang et al. on page 1967—1974.

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来源期刊

Chinese Journal of Chemistry 化学-化学综合

CiteScore

8.80

自引率

14.80%

发文量

422

审稿时长

1.7 months

期刊介绍： The Chinese Journal of Chemistry is an international forum for peer-reviewed original research results in all fields of chemistry. Founded in 1983 under the name Acta Chimica Sinica English Edition and renamed in 1990 as Chinese Journal of Chemistry, the journal publishes a stimulating mixture of Accounts, Full Papers, Notes and Communications in English.