Performance of SCAN density functional for a set of ionic liquid ion pairs

Abstract

Computational chemistry is a powerful tool for the discovery of novel materials. In particular, it is used to simulate ionic liquids in search of electrolytes for electrochemical applications. Herein, the choice of the computational method is not trivial, as it has to be both efficient and accurate. Density functional theory methods with appropriate corrections for the systematic weaknesses can give precision close to that of the post-Hartree-Fock coupled cluster methods with a fraction of their cost. Thence, we have evaluated the performance of a recently developed nonempirical strongly constrained and appropriately normed (SCAN) density functional on electronic structure calculations of ionic liquid ion pairs. The performance of SCAN and other popular functionals (PBE, M06-L, B2PLYP) among with Grimme's dispersion correction and Boys-Bernardi basis set superposition error correction was compared to DLPNO-CCSD(T)/CBS. We show that SCAN reproduces coupled-cluster results for describing the employed dataset of 48 ion pairs.density functional theory, dispersion correction, ionic liquids, self-interaction error, strongly constrained and appropriately normed 1 | I N TR ODU C TI ON Ionic liquids are promising solvents that have been extensively studied over the last few decades. Their tunable properties make them advantageous candidates for various electrochemical applications. [1,2] The high price of commercially available ionic liquids remains an obstacle for an extensive use of ionic liquids. [3] However, this is also a strong stimulus for the search of advanced ionic liquids. [1] A useful toolkit for studying ionic liquids includes various computer simulation methods. [4] Simulations have been growing in popularity, hand in hand with an exponential increase in the computational capabilities. [5,6] For instance, computational screening allows estimating the properties of many candidates even prior to their synthesis. [7][8][9][10][11] Therefore, it helps to determine, which candidates are the best for a given application. Also, molecular dynamics simulations provide an insight into both structure and dynamics of specific ionic liquids both in bulk and near various interfaces. [5,[12][13][14][15][16] However, they require careful parametrization of the force fields used. [5,12,[17][18][19][20] On the contrary, without parametrization, quantum mechanical calculations allow rapid exploration of the space of ionic liquids, which is vast due to numerous possible anion-cation combinations. [4,21] Fast computational methods are essential for both high-throughput screening and large-size simulations. At the same time, the methods have to be accurate enough to capture all of the physical interactions within the ionic liquids. The density functional theory (DFT) methods offer a good trade-off between speed and accuracy. [4,22,23] The success of DFT depends whether employed exchange-correlation functional can adequately describe the system of interest. [24] In practice, the necessity for faster and scalable methods attracts t...

Calculation of core‐level electron spectra of ionic liquids

“…[ 52,53 ] Compared to the most accurate functionals, PBE shows slightly lower performance in respect of the dissociation energy for the chosen set of IL ion pairs. [ 54–56 ] However, PBE is sufficiently fast and accurate for ΔKS‐type calculations that give the total energy difference between the ground state and the first core‐excited state. [ 57,58 ] To obtain the 1s KS orbital eigenvalues of the selected atoms, we generated the all‐electron setups using the GPAW setup tool with the parameter –core = “”.…”

“…The gas‐phase relaxed structures of ion pairs were taken from refs. [28,55] where they were optimized with B2PLYP double hybrid functional and triple‐zeta basis set. Forty different IL ion pairs, all depicted in Figure 1, were formed by combining eight anions (B(CN) 4 − , TFSI − , FSI − , PF 6 − , BF 4 − , Cl − , Br − , I − ) with five cations (EMIm + , BMIm + , BMPyr + , BPy + , TEPA + ).…”

Predicting Melting Points of Biofriendly Choline-Based Ionic Liquids with Molecular Dynamics

“…As the GHELPG analysis does not depend much on the basis set, one of the simplest option (Def2-SVP) was chosen. For the same reason PBE density functional, showing a moderate performance on ILs [34,35], was used.…”

NaRIBaS—A Scripting Framework for Computational Modeling of Nanomaterials and Room Temperature Ionic Liquids in Bulk and Slab

“…There is a vast amount of possible combinations of cations and anions that are potentially useful in practical application [19]. Moreover, there are many basis sets, functionals as well as computational approximations [20]. NaRIBaS is a suitable tool for such combinatorial exercise; it can be used for conducting repetitive calculations and organizing systematic data storage.…”

“…NaRIBaS is a suitable tool for such combinatorial exercise; it can be used for conducting repetitive calculations and organizing systematic data storage. Furthermore, the flexible task routines can serve different purposes with only minor retuning, as in References [19][20][21]. In Reference [19], we applied the NaRIBaS workflow from Figure 3 to predict the relative electrochemical stabilities and the activation energies of the viscosity of 48 ILs.…”