Departamento de Ingeniería Química, Universidad Complutense de Madrid, 28040 Madrid, Spain.
Phys Chem Chem Phys. 2010 Feb 28;12(8):1991-2000. doi: 10.1039/b920651p. Epub 2010 Jan 18.
A COSMO-RS descriptor (S(sigma-profile)) has been used in quantitative structure-property relationship (QSPR) studies by a neural network (NN) for the prediction of empirical solvent polarity E(T)(N) scale of neat ionic liquids (ILs) and their mixtures with organic solvents. S(sigma-profile) is a two-dimensional quantum chemical parameter which quantifies the polar electronic charge of chemical structures on the polarity (sigma) scale. Firstly, a radial basis neural network exact fit (RBNN) is successfully optimized for the prediction of E(T)(N), the solvatochromic parameter of a wide variety of neat organic solvents and ILs, including imidazolium, pyridinium, ammonium, phosphonium and pyrrolidinium families, solely using the S(sigma-profile) of individual molecules and ions. Subsequently, a quantitative structure-activity map (QSAM), a new concept recently developed, is proposed as a valuable tool for the molecular understanding of IL polarity, by relating the E(T)(N) polarity parameter to the electronic structure of cations and anions given by quantum-chemical COSMO-RS calculations. Finally, based on the additive character of the S(sigma-profile) descriptor, we propose to simulate the mixture of IL-organic solvents by the estimation of the S(sigma-profile)(Mixture) descriptor, defined as the weighted mean of the S(sigma-profile) values of the components. Then, the E(T)(N) parameters for binary solvent mixtures, including ILs, are accurately predicted using the S(sigma-profile)(Mixture) values from the RBNN model previously developed for pure solvents. As result, we obtain a unique neural network tool to simulate, with similar reliability, the E(T)(N) polarity of a wide variety of pure ILs as well as their mixtures with organic solvents, which exhibit significant positive and negative deviations from ideality.
一种 COSMO-RS 描述符（S（sigma-profile））已被用于神经网络（NN）的定量结构-性质关系（QSPR）研究，以预测经验溶剂极性 E（T）（N）尺度的纯离子液体（ILs）及其与有机溶剂的混合物。S（sigma-profile）是一种二维量子化学参数，用于量化化学结构在极性（sigma）尺度上的极性电子电荷。首先，径向基神经网络精确拟合（RBNN）成功地优化了 E（T）（N）的预测，E（T）（N）是各种纯有机溶剂和 ILs 的溶剂化参数，包括咪唑鎓、吡啶鎓、铵、鏻和吡咯烷鎓家族，仅使用单个分子和离子的 S（sigma-profile）。随后，提出了一种新的概念，即定量结构活性图（QSAM），作为理解 IL 极性的分子理解的有用工具，通过将 E（T）（N）极性参数与量子化学 COSMO-RS 计算给出的阳离子和阴离子的电子结构相关联。最后，基于 S（sigma-profile）描述符的可加性，我们建议通过估计混合物的 S（sigma-profile）（混合物）描述符来模拟 IL-有机溶剂混合物，定义为混合物中各成分 S（sigma-profile）值的加权平均值。然后，使用之前为纯溶剂开发的 RBNN 模型中的 S（sigma-profile）（混合物）值，准确预测包括 IL 在内的二元溶剂混合物的 E（T）（N）参数。结果，我们获得了一种独特的神经网络工具，可以以相似的可靠性模拟各种纯 IL 以及它们与有机溶剂的混合物的 E（T）（N）极性，这些混合物表现出显著的正偏离和负偏离理想情况。
