Cyclopropenyl Anion: An Energetically Nonaromatic Ion

Kass, S. R. J. Org. Chem. 2013, 78, 7370-7372

Contributed by Steven Bachrach.
Reposted from Computational Organic Chemistry with permission

The concept of antiaromaticity is an outgrowth of the well-entrenched notion or aromaticity. While 4n+2 π-electron systems are aromatic, 4n π-electron systems should be antiaromatic. That should mean that antiaromatic systems are unstable. The cyclopropenyl anion 1a has 4 π-electrons and should be antiaromatic. Kass has provided computational results that strongly indicate it is not antiaromatic!¹

Let’s first look at the 3-cyclopropenyl cation 1c. Kass has computed (at both G3 and W1) the hydride affinity of 1c-4c. The hydride affinities of the latter three compounds plotted against the C=C-C⁺ angle is linear. The hydride affinity of 1c however falls way below the line, indicative of 1c being very stable – it is aromatic having just 2 π-electrons.

A similar plot of the deprotonation enthalpies leading to 1a-4d vs. C=C-C^- angle is linear including all four compounds. If 1a where antiaromatic, one would anticipate that the deprotonation energy to form1a would be much greater than expected simply from the effect of the smaller angle. Kass suggests that this indicates that 1a is not antiaromatic, but just a regular run-of-the-mill (very) reactive anion.

A hint at what’s going on is provided by the geometry of the lowest energy structure of 1a, shown in Figure 1. The molecule is non-planar, having C_s symmetry. A truly antiaromatic structure should be planar, really of D_3h symmetry. The distortion from this symmetry reduces the antiaromatic character, in the same way that cyclobutadiene is not a perfect square and that cyclooctatraene is tub-shaped and not planar. So perhaps it is more fair to say that 1a has a distorted structure to avoid antiaromaticity, and that the idealized D_3h structure, does not exist because of its antiaromatic character.

Figure 1. G3 optimized geometry of 1a.

References

(1) Kass, S. R. "Cyclopropenyl Anion: An Energetically Nonaromatic Ion," J. Org. Chem. 2013, 78, 7370-7372, DOI: 10.1021/jo401350m.

InChIs

1a: InChI=1S/C3H3/c1-2-3-1/h1-3H/q-1
InChIKey=IBTMQWIWZUYLHW-UHFFFAOYSA-N

1c: InChI=1S/C3H3/c1-2-3-1/h1-3H/q+1
InChIKey=IPKCFGQXHZKYLH-UHFFFAOYSA-N

This work is licensed under a Creative Commons Attribution-NoDerivs 3.0 Unported License.

Bulk Liquid Water at Ambient Temperature and Pressure from MP2 Theory

Mauro Del Ben, Mandes Schönherr, Jürg Hutter, and Joost VandeVondele. J. Phys. Chem. Lett. 2013, 4, 3753−3759. DOI: 10.1021/jz401931f
Contributed by François-Xavier Coudert.

Reprinted with permission from doi:10.1021/jz401931f
Copyright 2013 American Chemical Society.

Let's start with the obvious: molecular simulation of liquid water is a very challenging, yet very important, part of our field. While MP2 (second-order perturbation theory) gives a highly accurate description of water-water interactions in water clusters, it was so far too computationally expensive to perform decent-scale molecular dynamics and Monte Carlo simulations of bulk liquid water.

Well, no more. Using large HPC resources, in particular the European PRACE Research Infrastructure and the Swiss National Supercomputer Centre, Mauro Del Ben et al. report in J. Phys. Chem. Lett. the first “truly first-principles simulation of liquid water in the NpT ensemble”. They performed a isobaric-isothermal Monte Carlo simulation, at the MP2 level, of 64 water molecules in a periodic simulation cell, under ambient conditions. The resulting density and structure of the liquid water are quite good, and are contrasted in particular with the less-than-stellar densities yielded by DFT-based methods.

These results represent the latest step in a series of papers these past few years, harnessing the ever-growing power of HPC capabilities to test the validity of quantum chemical calculations for the description of bulk liquid water. Some of the earlier episodes can be read here:

• McGrath, M. J.; Siepmann, J. I.; Kuo, I.-F. W.; Mundy, C. J.; VandeVondele, J.; Hutter, J.; Mohamed, F.; Krack, M. “Isobaric−Isothermal Monte Carlo Simulations from First Principles: Application to Liquid Water at Ambient Conditions”. ChemPhysChem 2005, 6, 1894−1901.
• Schmidt, J.; VandeVondele, J.; Kuo, I.-F. W.; Sebastiani, D.; Siepmann, J. I.; Hutter, J.; Mundy, C. J. “Isobaric−Isothermal Molecular Dynamics Simulations Utilizing Density Functional Theory: An Assessment of the Structure and Density of Water at Near-Ambient Conditions”. J. Phys. Chem. B 2009, 113, 11959−11964.
• Jonchiere, R.; Seitsonen, A. P.; Ferlat, G.; Saitta, A. M.; Vuilleumier, R. “Van der Waals Effects in Ab Initio Water at Ambient and Supercritical Conditions”. J. Chem. Phys. 2011, 135, 154503.

Equatorenes: Synthesis and Properties of Chiral Naphthalene, Phenanthrene, Chrysene, and Pyrene Possessing Bis(1-adamantyl) Groups at the Peri-position

Yamamoto, K.; Oyamada, N.; Xia, S.; Kobayashi, Y.; Yamaguchi, M.; Maeda, H.; Nishihara, H.; Uchimaru, T.; Kwon, E. J. Am. Chem. Soc. 2013,135, 16526

Contributed by Steven Bachrach.
Reposted from Computational Organic Chemistry with permission

Naphthalene, phenanthrene and pyrene are all planar aromatic compounds. How can substituted version be chiral, with the chirality present in the aromatic portion of the molecule? The answer is provided by Yamaguchi and Kwon.¹ They prepared peri-substituted analogues with the bulky adamantly group as the substituents. This bulky requires one adamantyl group to be position above the aromatic plane and the other below the plane, as in 1 and 2.

1

2

These molecules and two other examples were prepared in their optically pure form. B3LYP/6-31G(d) computations were performed on both of these structures (shown in Figure 1), but computations are a minor component of the work. These structures do show the out-of-plane distortions at C₁ and C₈, also apparent in the crystal structures. Computations of naphthalene and 1,8-dimethylnaphthalene show a planar naphthalene backbone, but i­-propyl substitution does force the substituents out of plane.

1

2

Figure 1. B3LYP/6-31G(d) optimized structures of 1 and 2.

These types of systems continue to subject the notion of “aromaticity” to serious scrutiny.

References

(1) Yamamoto, K.; Oyamada, N.; Xia, S.; Kobayashi, Y.; Yamaguchi, M.; Maeda, H.; Nishihara, H.; Uchimaru, T.; Kwon, E. "Equatorenes: Synthesis and Properties of Chiral Naphthalene, Phenanthrene, Chrysene, and Pyrene Possessing Bis(1-adamantyl) Groups at the Peri-position," J. Am. Chem. Soc. 2013,135, 16526-16532, DOI: 10.1021/ja407800e.

InChIs

1: InChI=1S/C30H36/c1-3-25-4-2-6-27(30-16-22-10-23(17-30)12-24(11-22)18-30)28(25)26(5-1)29-13-19-7-20(14-29)9-21(8-19)15-29/h1-6,19-24H,7-18H2
InChIKey=QNPJKZPPLCPHSS-UHFFFAOYSA-N

2: InChI=1S/C36H38/c1-2-27-4-5-28-6-7-30(35-15-21-8-22(16-35)10-23(9-21)17-35)34-31(14-29(3-1)32(27)33(28)34)36-18-24-11-25(19-36)13-26(12-24)20-36/h1-7,14,21-26H,8-13,15-20H2
InChIKey=DKRBDGNWYTWNHL-UHFFFAOYSA-N

This work is licensed under a Creative Commons Attribution-NoDerivs 3.0 Unported License.

Electron correlation, zero-point vibrational and temperature effects in Nuclear Magnetic Resonance spectroscopy

A.M. Teale, O.B. Lutnæs, T. Helgaker, D.J. Tozer, J. Gauss, Journal of Chemical Physics 2013, 138, 024111 (Pay-wall) and
J. Kaminský, M. Buděšínský, S. Taubert, P. Bouřa, M. Straka, Physical Chemistry Chemical Physics 2013, 15, 9223-9230 (Open Access)
Contributed by Marcel Swart

Last year has seen two important contributions in the field of determination of NMR chemical shifts by theoretical chemistry. More and more it is recognized that the computational prediction of ¹H and ¹³C chemical shifts is a useful tool for natural product, mechanistic, and synthetic organic chemistry.^[1] There are however doubts about how accurate these results are, and if any chemically relevant conclusions can be drawn from them.

The first paper^[2] compares the chemical shifts as obtained by both density functional theory and wavefunction theory (RHF, CCSD, CCSD(T), extrapolated) for a total of 28 molecules (for which previously already rotational g-tensors and magnetizabilities were computed^[3]). The authors also included zero-point vibrational effects on the computed chemical shieldings, and used extrapolation techniques to estimate uncertainties related to basis-set incompleteness^[4]. First, the authors established an accurate benchmark set of data, for which the accuracy was established by comparison with experimental data (including zero-point vibrational corrections). They found good agreement between CCSD(T)/aug-cc-pCVQZ and experiment (empirical equilibrium values), with a mean-absolute-error of 2.9 ppm for the chemical shieldings. Afterwards, these reference data were used to compare how well a variety of density functionals was able to reproduce them, with a sobering conclusion: "None of the existing approximate functionals provide an accuracy competitive with that provided by CCSD or CCSD(T) theory".^[2] The best performing functional (as shown before) was KT2 with a mean-absolute-error compared to the CCSD(T) data (both with the same aug-cc-pCVQZ basis set) of 10.2 ppm.

The second paper^[5] has a completely different approach, and deals with the characterization of fullerenes. For this purpose, computational chemistry might be used, but one again should be sure about the methods used. The authors used quantum vibrational averaging, a dielectric continuum model for the solvent (CPCM, 1,1,2-trichloroethane), classical (MM3) and first-principle (BP86/def-SVP) molecular dynamics simulations, and did experiments. These authors used a different set of density functionals, and found the best results for wB97X-D/IGLO-III (a root-mean-square deviation compared to experiment of only 0.4 ppm). However, surprisingly, in the analysis of the dynamical part they used either BP86 or BHandHLYP for the NMR chemical shifts, even though these showed larger RMSD values of respectively 1.7 and 0.8 ppm. Moreover, big differences were found in the chemical shifts from the snapshots of the 1 ns classical MD, and those of the 1.2 ps first-principles MD. For the five different atom types these differences were found in the range 1.8-7.9 ppm.^[6]

References and notes

[1] M.W. Lodewyk, M.R. Siebert, D.J. Tantillo, Chem. Rev. 2012, 112, 1839-1862 [DOI 10.1021/cr200106v]
[2] A.M. Teale, O.B. Lutnæs, T. Helgaker, D.J. Tozer, J. Gauss, J. Chem. Phys. 2013, 138, 024111 [DOI 10.1063/1.4773016]
[3] O. B. Lutnæs, A. M. Teale, T. Helgaker, D. J. Tozer, K. Ruud, J. Gauss, J. Chem. Phys. 2009, 131, 144104 [DOI 10.1063/1.3242081]
[4] These formulas have been developed for energies; hence, their use for the direct extrapolation of molecular properties is less well founded, and indeed can not be used with a two-point extrapolation for prediction of the basis set limiting value.
[5] J. Kaminský, M. Buděšínský, S. Taubert, P. Bouřa, M. Straka, Phys. Chem. Chem. Phys. 2013, 15, 9223-9230 [DOI 10.1039/C3CP50657F]
[6] Strangely enough, while the CPCM solvent model only had a modest effect on the chemical shifts of 0.2-0.3 ppm, the authors showed that first-principles MD (FPMD) simulations including solvent effects (COSMO) led to drastically different results for the chemical shifts of snapshots (0.1-4.1 ppm) from those from FPMD without them.


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CP and BSSE

Contributed by Frank Jensen

A couple of recent papers illustrate the problems related to using the CP correction for reducing BSSE:

Ł. M. Mentel and E. J. Baerends, JCTC ASAP, DOI: 10.1021/ct400990u
Report that for He-He and Be-Be interaction potentials, the CP correction can be in the wrong direction. The cause is apparently that the basis sets are optimized for the atoms, and thus biased against the complex. The uncorrected interaction energy is thus underestimated and adding the CP correction further underbinds the complex.

Lori A. Burns, Michael S. Marshall, and C. David Sherrill, JCTC ASAP, DOI: 10.1021/ct400149j
Perform a benchmark study using MP2 and CCSD(T) with and without CP corrections, or the average, combined with basis set extrapolation for the A24 benchmark systems + a few extras. Their conclusion is that whether to use the CP, half the CP or no CP depend on the system, method and basis set. Their (weak) recommendation is to use half the CP correction for basis sets of aDZP or aTZP quality to 'avoid the worst errors incurred by either method', and the full CP for larger basis sets and extrapolations.

The latter study most likely has components of the first: A given (fixed) basis set will be (slightly) non-optimum for each fragment and the complex, but which fragment/complex that it is least optimum for will depend on the system and geometry. The inherent basis set over/under-binding of the complex will be modulated by the overbinding by the BSSE. Adding the CP estimate of the BSSE can then lead to either improvement or deterioration of the final binding energy. Noting that all three effects are small, the inherent basis set error can have either sign, the BSSE is always negative and the CP correction is always positive, the combined effect will have significant 'random' errors compared to the exact result, which in magnitude also is small.

Microscopic Insights into the NMR Relaxation-Based Protein Conformational Entropy Meter

Vignesh Kasinath, Kim A. Sharp, and A. Joshua Wand Journal of the American Chemical Society 2013, 135, 15092
Contributed by +Jan Jensen

Order parameters measured by NMR report on the local fluctuations of protein structures and should therefore be related to entropy. This study uses molecular dynamics (MD) simulations to obtain a quantitative relationship between conformational side chain entropy ($S_{sc}$) and Lipari-Szabo methyl group squared generalized order parameters ($O^2$).

First the authors demonstrate that MD simulations can reproduce experimentally measured $O^2$ values for 7 different proteins, with an $R^2$ of 0.92.

Second the authors demonstrate a linear correlation between the total $S_{sc}$ and computed $O^2$ values, both for methyl containing side chains ($R^2$ = 0.90) and for all side chains ($R^2$ = 0.91).

Based on these finding the authors re-analyzed data from two previously published studies, and extracted a similarly quantitative linear correlation ($R^2$ = 0.95) between the molecular entropy change ($\Delta S_{tot}-\Delta S_{solv}$) and the measured change in $O^2$ values for protein-protein and protein-DNA binding.  This suggest that both entropy changes are dominated by the changes in conformational side-chain entropy.

The method looks like a very powerful tool for obtaining detailed quantitative structural understanding of entropy changes in biomolecular processes.

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Computational Chemistry Highlights received 28,762 pageviews in 2013.

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