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首页> 外文期刊>The journal of physical chemistry, A. Molecules, spectroscopy, kinetics, environment, & general theory >pK(a) calculations of aliphatic amines, diamines, and aminoamides via density functional theory with a Poisson-Boltzmann continuum solvent model
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pK(a) calculations of aliphatic amines, diamines, and aminoamides via density functional theory with a Poisson-Boltzmann continuum solvent model

机译:使用Poisson-Boltzmann连续介质溶剂模型通过密度泛函理论计算脂肪族胺,二胺和氨基酰胺的pK(a)

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In order to make reliable predictions of the acid-base properties of macroligands with a large number of ionizable sites such as dendrimers, one needs to develop and validate computational methods that accurately estimate the acidity constants (pK(a)) of their chemical building blocks. In this article, we couple density functional theory (B3LYP) with a Poisson-Boltzmann continuum solvent model to calculate the aqueous pK(a) of aliphatic amines, diamines, and aminoamides, which are building blocks for several classes of dendrimers. No empirical correction terms were employed in the calculations except for the free energy of solvation of the proton (H+) adjusted to give the best match with experimental data. The use of solution-phase optimized geometries gives calculated pK(a) values in excellent agreement with experimental measurements. The mean absolute error is < 0.5 pK(a) unit in all cases. Conversely, calculations for diamines and aminoamides based on gas-phase geometries lead to a mean absolute error > 0.5 pK(a) unit compared to experimental measurements. We find that geometry optimization in solution is essential for making accurate pK(a) predictions for systems possessing intramolecular hydrogen bonds.
机译:为了可靠预测具有大量可电离位点的大分子配体的酸碱性质,例如树枝状大分子,需要开发和验证能准确估算其化学结构单元酸度常数(pK(a))的计算方法。 。在本文中,我们将密度泛函理论(B3LYP)与Poisson-Boltzmann连续介质溶剂模型耦合,以计算脂肪族胺,二胺和氨基酰胺的水性pK(a),它们是几类树枝状聚合物的组成部分。除了调整的质子溶剂化自由能(H +)与实验数据最匹配外,计算中未使用经验校正项。固溶相优化几何结构的使用可得出与实验测量值极佳的计算得出的pK(a)值。在所有情况下,平均绝对误差均<0.5 pK(a)单位。相反,基于气相几何结构的二胺和氨基酰胺计算得出的平均绝对误差与实验测量值相比> 0.5 pK(a)单位。我们发现解决方案中的几何优化对于对具有分子内氢键的系统做出准确的pK(a)预测至关重要。

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