Evaluation of Density-Functional Tight-Binding Methods for Simulation of Protic Molecular Ion Pairs

IF 4.8 3区 化学 Q2 CHEMISTRY, MULTIDISCIPLINARY Journal of Computational Chemistry Pub Date : 2025-02-13 DOI:10.1002/jcc.70064
Tyler Walker, Van-Quan Vuong, Stephan Irle, Jihong Ma
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Abstract

In this work, we benchmark the accuracy of the density-functional tight-binding (DFTB) method, namely the long-range corrected second-order (LC-DFTB2) and third-order (DFTB3) models, for predicting energetics of imidazolium-based ionic liquid (IL) ion pairs. We compare the DFTB models against popular density functionals such as LC-ωPBE and B3LYP, using ab initio domain-based local pair-natural orbital coupled cluster (DLPNO-CC) energies as reference. Calculations were carried out in the gas phase, as well as in aqueous solution using implicit solvent methods. We find that the LC-DFTB2 model shows excellent performance in the gas phase and agrees well with reference energies in implicit solvent, often outperforming DFTB3 predictions for complexation energetics. Our study identifies a range of opportunities for use of the LC-DFTB method and quantifies its sensitivity to protonation states and the types of chemical interactions between ion pairs.

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模拟质子分子离子对的密度-功能紧密结合方法的评价
在这项工作中,我们对密度-功能紧密结合(DFTB)方法的准确性进行了基准测试,即远程校正二阶(LC-DFTB2)和三阶(DFTB3)模型,用于预测咪唑基离子液体(IL)离子对的能量学。我们将DFTB模型与LC-ωPBE和B3LYP等流行的密度泛函进行了比较,以从头算域为基础的局部对自然轨道耦合簇(DLPNO-CC)能量为参考。采用隐式溶剂法对气相和水溶液进行了计算。我们发现LC-DFTB2模型在气相中表现出优异的性能,并且与隐式溶剂中的参考能量很好地吻合,通常优于DFTB3对络合能量的预测。我们的研究确定了LC-DFTB方法的一系列使用机会,并量化了其对质子化状态和离子对之间化学相互作用类型的敏感性。
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来源期刊
CiteScore
6.60
自引率
3.30%
发文量
247
审稿时长
1.7 months
期刊介绍: This distinguished journal publishes articles concerned with all aspects of computational chemistry: analytical, biological, inorganic, organic, physical, and materials. The Journal of Computational Chemistry presents original research, contemporary developments in theory and methodology, and state-of-the-art applications. Computational areas that are featured in the journal include ab initio and semiempirical quantum mechanics, density functional theory, molecular mechanics, molecular dynamics, statistical mechanics, cheminformatics, biomolecular structure prediction, molecular design, and bioinformatics.
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