Accurate prediction of energetic properties of ionic liquid clusters using a fragment-based quantum mechanical method

Literature Information

Publication Date 2017-07-12
DOI 10.1039/C7CP03356G
Impact Factor 3.676
Authors

Jinfeng Liu


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Abstract

Accurate prediction of physicochemical properties of ionic liquids (ILs) is of great significance to understand and design novel ILs with unique properties. This study employed the electrostatically embedded generalized molecular fractionation (EE-GMF) method for accurate energy calculation of IL clusters. The accuracy and efficiency of the EE-GMF method are systematically assessed at different ab initio levels (including HF, DFT and MP2) with diverse basis sets. With the fixed charge model for the embedding field, the deviations of the EE-GMF approach from conventional full system calculations are within 2.58 kcal mol−1 for all IL clusters with up to 30 ion pairs (720 atoms), tested in this study. Moreover, this linear-scaling fragment quantum mechanical (QM) method can significantly reduce the total computational cost for post-HF methods. The EE-GMF approach is well-suited for studying the energetic, structural and dynamical properties of ILs using high-level ab initio theories.

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Physical Chemistry Chemical Physics

Physical Chemistry Chemical Physics
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Physical Chemistry Chemical Physics (PCCP) is an international journal co-owned by 19 physical chemistry and physics societies from around the world. This journal publishes original, cutting-edge research in physical chemistry, chemical physics and biophysical chemistry. To be suitable for publication in PCCP, articles must include significant innovation and/or insight into physical chemistry; this is the most important criterion that reviewers and Editors will judge against when evaluating submissions. The journal has a broad scope and welcomes contributions spanning experiment, theory, computation and data science. Topical coverage includes spectroscopy, dynamics, kinetics, statistical mechanics, thermodynamics, electrochemistry, catalysis, surface science, quantum mechanics, quantum computing and machine learning. Interdisciplinary research areas such as polymers and soft matter, materials, nanoscience, energy, surfaces/interfaces, and biophysical chemistry are welcomed if they demonstrate significant innovation and/or insight into physical chemistry. Joined experimental/theoretical studies are particularly appreciated when complementary and based on up-to-date approaches.

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