Modeling Carbon Dioxide Vibrational Frequencies in Ionic Liquids: I. Ab Initio Calculations.
Eric J BerquistClyde A DalyThomas BrinzerKrista K BullardZachary M CampbellSteven A CorcelliSean Garrett-RoeDaniel S LambrechtPublished in: The journal of physical chemistry. B (2016)
This work elucidates the molecular binding mechanism of CO2 in [C4C1IM][PF6] ionic liquid (IL) and its interplay with the CO2 asymmetric stretch frequency ν3, and establishes computational protocols for the reliable construction of spectroscopic maps for simulating ultrafast 2D-IR data of CO2 solvated in ILs. While charge transfer drives the static frequency shift between different ionic liquids ( J. Chem. Phys. 2015 , 142 , 212425 ), we find here that electrostatic and Pauli repulsion effects dominate the dynamical frequency shift between different geometries sampled from the finite-temperature dynamics within a single ionic liquid. This finding is also surprising because dispersion interactions dominate the CO2-IL interaction energies, but are comparably constant across different geometries. An important practical consequence of this finding is that density functional theory is expected to be sufficiently accurate for constructing potential energy surfaces for CO2 in [C4C1IM][PF6], as needed for accurate anharmonic calculations to construct a reliable spectroscopic map. Similarly, we established appropriate computational and chemical models for treating the extended solvent environment. We found that a QM/MM treatment including at least 2 cation-ion pairs at the QM level and at least 32 pairs at the MM level is necessary to converge vibrational frequencies to within 1 cm-1. Using these insights, this work identifies a computational protocol as well as a chemical model necessary to construct accurate spectroscopic maps from first principles.
Keyphrases
- ionic liquid
- density functional theory
- molecular docking
- carbon dioxide
- molecular dynamics
- room temperature
- high resolution
- molecular dynamics simulations
- randomized controlled trial
- electronic health record
- big data
- escherichia coli
- machine learning
- biofilm formation
- artificial intelligence
- energy transfer
- dna binding
- candida albicans
- solid state
- replacement therapy
- monte carlo