On the aromatic stabilization of corannulene and coronene

Dobrowolski, M. A.; Ciesielski, A.; Cyranski, M. K. Phys. Chem. Chem. Phys., 2011, 13, 20557-20563 (Paywall)
Contributed by Steven Bachrach.
Reposted from Computational Organic Chemistry with permission


How should one assess the aromatic stabilization energy of non-planar compounds like corannulene 1? The standard approach might be to employ the homodesmotic reaction, like Reaction 1. The energy of this reaction is however quite different whether one chooses s-cis or s-trans butadiene: 67.5 kcal mol^-1 with the former and 14.8 kcal mol^-1 with the latter. Exactly how one balances the total number of cis/trans relationships is problematic, but worse still is that Reaction 1 does not remove the effect of strain and non-planarity of 1.

1

2

Reaction 1
15 H₂C=CH-CH=CH₂ + 20 H₂C=C(CH₃)₂ → 1 + 10 H₂C=CH₂ + 20 H₂C=CH-CH₃

Dobrowolski, Ciesielski and Cyranski¹ propose a series of reactions that extend the isomerization stabilization energy concept of Schleyer. References are chosen that involve fixed alternate polyenes by appending methylene groups, creating radialene-like compounds. Reaction 2 and 3 are two such reactions that attempt to remove strain and non-planarity effects along with balancing the cis/trans relationships and potential H-H clashes between the pendant methylene groups. They report an additional 18 variations, because there is no unique method for portioning these effects.

Reaction 2

Reaction 3

Using B3LYP/6-311G** energies with zero-point vibrational energy, the reaction energies are 46.7 and 46.3 kcal mol^-1 for Reactions 2 and 3, respectively. Using all of the variations, the mean value is 44.7 kcal mol^-1 with a standard deviation of only 1.2 kcal mol^-1. It is clear that corranulene has a rather substantial artomatic stabilization energy, reflecting its decided aromatic character.
In a similar vein, they have also estimated the aromatic stabilization energy of coronene 2 as 58.4 kcal mol^-1, which, while clearly demonstrating the 2 is aromatic, it does not express any “superaromaticity”.

References

(1) Dobrowolski, M. A.; Ciesielski, A.; Cyranski, M. K. "On the aromatic stabilization of corannulene and coronene," Phys. Chem. Chem. Phys., 2011, 13, 20557-20563, DOI: 10.1039/C1CP21994D

InChIs

1: InChI=1S/C21H14/c1-3-13-6-7-15-10-11-16-9-8-14-5-4-12(2)17-18(13)20(15)21(16)19(14)17/h3-11H,1H2,2H3
InChIKey=ZJQHTVPYWDRMLD-UHFFFAOYSA-N

2: InChI=1S/C24H12/c1-2-14-5-6-16-9-11-18-12-10-17-8-7-15-4-3-13(1)19-20(14)22(16)24(18)23(17)21(15)19/h1-12H
InChIKey=VPUGDVKSAQVFFS-UHFFFAOYSA-N



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Computational Design and Selection of Optimal Organic Photovoltaic Materials

Noel M. O’Boyle, Casey M. Campbell and Geoffrey R. Hutchison Journal of Physical Chemistry C 2011, 115, 16200 (Paywall)

Quantum chemistry for high throughput screening
In this work over 90,000 pi-conjugated copolymer were computationally screened for new and efficient organic photo-voltaics. Not being an expert in organic photo-voltaics I highlight this paper as a very interesting example of what I believe is an important emerging trend in the use of quantum chemistry: efficient high through-put screening of molecules for desirable properties.  Computers and software have now reached a point where this is computationally feasible to perform computations on thousands of molecules and the challenge is now to make it practically possible, i.e. to find the right combinations of methods and automate their use.

How does one construct 90,000 geometries?  Cheminformatics meets quantum chemistry
OpenBabel is used to construct the 3D structure of each polymer, starting from its SMILES string, and to find the lowest energy conformation using a weighted rotor-search and the MMFF94 force field.  This lowest energy conformation is then minimized with PM6 and used to compute energies and oscillator strengths of the 15 lowest- energy electronic transitions with ZINDO/S, which forms the basis for estimating the energy conversion efficiency (see the paper for more details).  The required CPU time is  8-10 minutes per polymer on a single core.  To put that number in perspective: using 100 cores, 100,000 polymers can be screened in roughly a week.

Starting from 131 different monomers, all possible (19,701) dimers are made and these dimers are then used to construct the corresponding 58,707 tetramers. (A brief description on how exactly this was scripted would have been a welcome addition to the supplementary materials).  The energy conversion efficiency was computed for all these dimer and tetramers.  These results were used to calibrate a genetic search algorithm that was used to identify hexamers and octamers with high energy conversion efficiency without doing an exhaustive search.

Promising candidates and new strategies
The current state-of-the-art is ca 8% energy conversion efficiency.  This study found 621 polymers (mostly hexamers and octamers) with greater than 9%  and 2 with greater than 11 % energy conversion efficiency.  (As the authors point out these polymers still need to be filtered for solubility, crystal packing, and other factors.)  Just as interestingly new design strategies emerged:

"Our analysis of component monomers, dimers, and the copolymer sequence demonstrates important design rules for copolymer photovoltaics. Most importantly, the conventional picture of combining a strong donor and strong acceptor into an alternating copolymer is found to frequently yield poor energy- level alignment. Instead, our top hexamers and octamers reflect a decreased optical band gap due to high coupling between the two-component monomers, not solely due to particular HOMO or LUMO energies of the monomers themselves."

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

Energy Decomposition Analysis in Solution Based on the Fragment Molecular Orbital Method

D. G. Fedorov, K. Kitaura, J. Phys. Chem. A 116 (2012) 704-719.


The solvent screening has long been described relying on the dielectric constants taken arbitrarily based on "experience". In this work the dielectric constants are derived from ab initio calculations using the charges induced by the solute on the solvent surface in the polarizable continuum model. These charges uniquely define the solvent screening energy, and also the dielectric constant.

Induced solvent charges δ surrounding two subsystems I and J.

[Reprinted with permission from the Journal of Physical Chemistry A.  Copyright (2012) American Chemical Society.]

For the rather trivial case of two interacting systems (sodium and chloride ions), the analysis provides rich physical insight into the solvation process, and the induced charge quenching effect is observed, caused by the partial loss of the solvation shell and the potential pressure of the nearby ion.
The real challenge for a physical interpretation is found in systems with multiple fragments, in which case strong many-body electrostatic effects lead to  very interesting and peculiar results, such as a partial solvent screening reduced by the pressure of the solute-induced electrostatic potential of nearby fragments.

The solvent screening of the electrostatic interactions between charged residues in a small protein chignolin (PDB: 1UAO) is discussed based on ab initio (FMO) calculations in solution.

Amino acid-catalyzed aldol and Michael reactions

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


Here are a couple of articles describing computational approaches to catalytic enantioselective reactions using variations upon the classic proline-catalyzed aldol reaction of List and Barbas¹ that started the whole parade. I have discussed the major computational papers on that system in my book (Chapter 5.3).

Yang and Wong² investigated the proline-catalyzed nitro-Michael reaction, looking at four examples, two with aldehydes and two with ketones (Reactions 1-4).

Reaction 1

Reaction 2

Reaction 3

Reaction 4

These four reactions were examined at MP2/311+G**//M06-2x/6-31G**, and PCM was also applied. The key element of this study is that they examined two different types of transition states: (a) based on the Houk-List model involving a hydrogen bond and (b) an electrostatic based model with no hydrogen bond. These are sketched in Scheme 1. For each of the reactions 1-4 there are 8 located transition states differing in the orientation of the attack on to the syn or anti enamine.

Scheme 1. TS models

Model A

Model B

The two lowest energy TS are shown in Figure 1. TS1-β1-RS is the lowest TS and it leads to the major enantiomer. The second lowest TS, TS1-β3-SR, lies 2.9 kJ mol^-1 above the other TS, and it leads to the minor enantiomer. This lowest TS is of the Houk-List type (Model A) while the other TS is of the Model B type. The enthalpies of activation suggest an ee of 54%, in reasonable agreement with experiment.

TS1-β1-RS

TS1-β1-RS

Figure 1. M06-2x/6-31G** optimized geometries of TS1-β1-RS and TS1-β1-RS.

The computations of the other three reactions are equally good in terms of agreement with experiment, and importantly the computations indicate the reversal of stereoselection between the aldehydes and the ketones. These computations clearly implicate both the Houk-List and the non-hydrogen bonding TSs in the catalyzed Michael additions.

Houk in collaboration with Scheffler and Mahrwald investigate the use of histidine as a catalyst for the asymmetricaldol reaction.³ Examples of the histidine-catalyzed aldol are shown in Reactions 5-7.

	Reaction 5
	Reaction 6
	Reaction 7

The interesting twist here is whether the imidazole can also be involved in hydrogen bonding to the acceptor carbonyl group, serving the purpose of the carboxylic acid group in the Houk-List TS when proline is the catalyst (Model A). The transition states for these and a few other reactions were computed at M06-2x/6-31+G(d,p) including with the SMD continuum solvent model for water. The two lowest energy TSs for the reaction of isobutyraldehde and formaldehyde are shown in Figure 2; TS1 has the carboxylic acid group as the hydrogen donor while the imidazole is the donor in TS2. Of note is that these two TS are isoenergetic, indicating that both modes of stabilization are at play with histidine as the catalyst.

TS1

TS2

Figure 2. M06-2x/6-31+G(d,p) geometries of the two lowest energy TSs for the reaction of isobutyraldehde and formaldehyde catalyzed by histidine.

The possible TSs for Reactions 5-7 were also located. For example, with Reaction 5, the lowest energy TS involves the imidazole as the hydrogen donor and it leads to the major product. The lowest energy TS that leads to the minor product involves the carboxylic acid as the donor. The computed ee’s for Reactions 5-7 are in very good, if not excellent, agreement with the experimental values. The study should spur further activity in which one might tune the stereoselectivity by using catalysts with multiple binding opportunities.


References

(1) List, B.; Lerner, R. A.; Barbas, C. F., III; "Proline-Catalyzed Direct Asymmetric Aldol Reactions," J. Am. Chem. Soc., 2000, 122, 2395-2396, DOI: 10.1021/ja994280y.

(2) Yang, H.; Wong, M. W. "(S)-Proline-catalyzed nitro-Michael reactions: towards a better understanding of the catalytic mechanism and enantioselectivity," Org. Biomol. Chem.,
2012, 10, 3229-3235, DOI: 10.1039/C2OB06993H

(3) Lam, Y.-h.; Houk, K. N.; Scheffler, U.; Mahrwald, R. "Stereoselectivities of Histidine-Catalyzed Asymmetric Aldol Additions and Contrasts with Proline Catalysis: A Quantum Mechanical Analysis," J. Am. Chem. Soc. 2012, 134, 6286-6295, DOI: 10.1021/ja2118392




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ROBIA and Dolabriferol


• Currie, R. H.; Goodman, J. M. Angew. Chem. Int. Ed. 2012, 51, 4695-4697: "In Silico Inspired Total Synthesis of (-)-Dolabriferol"
• Socorro, I. M.; Goodman, J. M. J. Chem. Inf. Model. 2006, 46, 606-614: "The ROBIA Program for Predicting Organic Reactivity"
• Socorro, I. M.; Taylor, K.; Goodman, J. M. Org. Lett. 2005, 7, 3541-3544: "ROBIA: A Reaction Prediction Program"


In 2005, Goodman and co-workers introduced the ROBIA (Reaction Outcome By Informatics Analysis) program for predicting the possible products of organic reactions and assessing the kinetic and/or thermodynamic feasibility of product formation. This program combines a series of rules based on typical reactivity patterns of certain organic functional groups with molecular mechanics and/or quantum chemical energy calculations on predicted products and/or transition state structures for possible reactions. Using this program, Goodman and co-workers predicted that (-)-dolabriferol might be formed - both biosynthetically and possibly synthetically - by a retro-Claisen reaction of a polyketide-derived precursor, i.e., they predicted that dolabriferol would likely be one of the major thermodynamic products of such a reaction. Now, Goodman and co-workers describe in ACIE a laboratory synthesis of (-)-dolabriferol that involves just such a (biomimetic) reaction (of a suitably protected precursor). This report not only showcases the utility of their modeling approach in the context of synthesis design, but also provides support for its utility in assessing the feasibility of biosynthetic proposals.

Quantitative NMR-Derived Interproton Distances Combined with Quantum Mechanical Calculations of 13C Chemical Shifts in the Stereochemical Determination of Conicasterol F, a Nuclear Receptor Ligand from Theonella swinhoei

Chini, M. G.; Jones, C. R.; Zampella, A.; D’Auria, M. V.; Renga, B.; Fiorucci, S.; Butts, C. P.; Bifulco, G. Journal of Organic Chemistry 2012, 77, 1489. (Paywall)
Contributed by Steven Bachrach.
Reposted from Computational Organic Chemistry with permission


Here is an interesting twist on using computations in conjunction with experimental NMR to solve for molecular structure. I have blogged a number of times on comparing computed chemical shifts with experimental values to identify structure, and also on using the comparison of computed and experimental coupling constants to accomplish this purpose.

Butts and Bifulco were interested in the structure of conicasterol F 1 and opted to make two sets of comparison.¹ The first uses the traditional approach of comparing the computed and experimental ¹³C chemical shifts. The second comparison uses the distances between protons, coming from the optimized structure and the rotating-frame nuclear Overhauser effect (ROE).

Standard analysis of the NMR spectra of 1 allowed for the determination of all of the stereochemistry except for the epoxy ring at C₈ and C₁₄. The possible options are shown as 1a and 1b. The optimized geometries (MPW1PW91/6-31G) of these two diastereomers are shown in Figure 1.

1a	1b
1a	1b

Figure 1. Optimized geometries of 1a and 1b.

Comparison of 15 distances between protons determined by the ROE experiment and by computation led to a mean absolute error of 7.8% for 1a and 3.0% for 1b, suggesting that the latter is the correct structure. Similar comparison was then made between the experimental chemical shifts of 12 of the carbon atoms with the computed values of the two isomers. The mean absolute error in the chemical shifts of 1a is 3.7ppm, but only 0.8 ppm for 1b. Both methods give the same conclusion: conicasterol F has structure 1b.

References

(1) Chini, M. G.; Jones, C. R.; Zampella, A.; D’Auria, M. V.; Renga, B.; Fiorucci, S.; Butts, C. P.; Bifulco, G., "Quantitative NMR-Derived Interproton Distances Combined with Quantum Mechanical Calculations of ¹³C Chemical Shifts in the Stereochemical Determination of Conicasterol F, a Nuclear Receptor Ligand from Theonella swinhoei," J. Org. Chem., 2012, 77, 1489-1496, DOI: 10.1021/jo2023763.


InChIs


1b: InChI=1/C29H46O4/c1-16(2)17(3)8-9-18(4)21-14-23(31)28-26(21,7)15-24-29(32-24)25(6)12-11-22(30)19(5)20(25)10-13-27(28,29)33-28/h16-18,20-24,30-31H,5,8-15H2,1-4,6-7H3/t17-,18-,20+,21-,22+,23+,24-,25+,26-,27+,28+,29+/m1/s1
InChIKey=XTWHHLLDFQALAM-XAIGNWORBC



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