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首页> 外文期刊>Physical chemistry chemical physics: PCCP >Prediction of non-ideal behavior of polarity/polarizability scales of solvent mixtures by integration of a novel COSMO-RS molecular descriptor and neural networks
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Prediction of non-ideal behavior of polarity/polarizability scales of solvent mixtures by integration of a novel COSMO-RS molecular descriptor and neural networks

机译：通过集成新型COSMO-RS分子描述符和神经网络来预测溶剂混合物的极性/极化度标度的非理想行为

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摘要

A new COSMO-RS descriptor (S_(σ-profile)) has been used in quantitative structure-property relationship (QSPR) studies based on neural networks (NN) for the prediction of polarity/ polarizability scales of pure solvents and mixtures. S_(σ-profile) is a two-dimensional quantum chemical parameter which quantifies the polar electronic charge on the polarity (σ) scale. Firstly, radial base neural networks (RBNN) are successfully optimized for the prediction of polarizability (SP) and polarity/polarizability (SPP) scales of pure solvents using the S_(σ-profile) of individual molecules. Subsequently, based on the additive character of the S_(σ-profile) parameter, we propose to simulate the solvents mixture by the estimation of S_(σ-profile)~(Mixture) descriptor, defined as the weighted mean of S_(σ-profile) values of the components. Then, the SPP parameters for binary and ternary mixtures are accurately predicted using the S_(σ-profile)~(Mixture) values into the RBNN model previously developed for pure solvents. As result, we obtain a unique neural network tool to simulate, with similar reliability, the polarity/polarizability of a wide variety of pure organic solvents as well as binary and ternary mixtures which exhibit significant deviations from ideality.
机译：一种新的COSMO-RS描述符（S_（σ-profile））已用于基于神经网络（NN）的定量结构-性质关系（QSPR）研究中，用于预测纯溶剂和混合物的极性/极化度范围。 S_（σ-profile）是二维量子化学参数，它以极性（σ）尺度量化极性电子电荷。首先，使用单个分子的S_（σ-profile）成功地优化了径向基神经网络（RBNN），用于预测纯溶剂的极化率（SP）和极性/极化率（SPP）规模。随后，基于S_（σ-profile）参数的加性，我们建议通过估计S_（σ-profile）〜（Mixture）描述符（定义为S_（σ-配置文件）的值。然后，使用S_（σ-profile）〜（Mixture）值到先前​​为纯溶剂开发的RBNN模型中，准确预测二元和三元混合物的SPP参数。结果，我们获得了一个独特的神经网络工具，以类似的可靠性模拟了多种纯有机溶剂以及二元和三元混合物的极性/极化度，这些二元和三元混合物显示出与理想情况显着偏离。

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