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引用次数: 0
摘要
将化学物质的振动模式分为阻碍内旋(HIR)和谐振振荡模式对于在广泛的温度范围内获得正确的热力学数据是非常重要的。在这项研究中,我们提出了一个多元线性支持向量机(SVM)模型来解决这个具有挑战性的二值分类问题。该模型的结果与逻辑回归的结果相似,比基于规则的方法的结果好2-5%。此外,线性支持向量机发现的特征数量也少于逻辑回归(5个对6个),这使得化学家更容易解释。并对这些差异作了详细的解释。这三个模型在Multi-Species Multi-Channel Software Suite (Duong et al., Int.)的GUI中实现。j .化学。Kinet, 2015, 564),以促进HIR模式的确定以及感兴趣的化学物质的热力学性质的计算。
Linear support vector machine to classify the vibrational modes for complex chemical systems
Classification of vibrational modes into hindered internal rotation (HIR) and harmonic oscillation modes is important to obtain correct thermodynamic data for a chemical species for a wide range of temperatures. In this study, we propose a multivariate linear support vector machine (SVM) model to solve this challenging binary classification problem. The results of the proposed model were found to be similar to those of logistic regression and 2-5% better than those of the rule-based method. Moreover, the number of features found by linear SVM was also fewer than that of logistic regression (five versus six), which makes it easier to be interpreted by chemists. The detailed explanation of such differences is also presented. The three models were implemented in the GUI of the Multi-Species Multi-Channel Software Suite (Duong et al., Int. J. Chem. Kinet, 2015, 564) to facilitate the determination of HIR modes as well as the calculation of thermodynamic properties for a chemical species of interest.