Tuesday, August 8, 2017

Mechanisms and Origins of Periselectivity of the Ambimodal [6 + 4] Cycloadditions of Tropone to Dimethylfulvene

Yu, P.; Chen, T. Q.; Yang, Z.; He, C. Q.; Patel, A.; Lam, Y.-h.; Liu, C.-Y.; Houk, K. N., J. Am. Chem. Soc. 2017, 139 (24), 8251-8258
Contributed by Steven Bacharach
Reposted from Computational Organic Chemistry with permission

Bispericyclic transition states arise when two pericyclic reactions merge to a common transition state. This leads to a potential energy surface with a bifurcation such that reactions that traverse this type of transition state will head towards two different products. The classic example is the dimerization of cyclopentadiene, involving two [4+2] Diels-Alder reactions. Unusual PESs are discussed in my book and in past blog posts.

Houk and coworkers have now identified a bispericyclic transition state involving two [6+4] cycloadditions.1 Reaching back to work Houk pursued as a graduate student with Woodward for inspiration, these authors examined the reaction of tropone 1 with dimethylfulvene 2. Each moiety can act as the diene or triene component of a [6+4] allowed cycloaddition:

The product with fulvene 2 as the 6 π-e component and tropone as the 4 π-e component [6F+4T] is 3, while reversing their participation in the [6T+4F] cycloaddition leads to 4. A variety of [4+2] reactions are also possible. All of these reactions were investigated at PCM/M06-2X/6-311+G(d,p)//B3LYP-D3/6-31G(d). The reaction leading to 3 is exothermic by 3.0 kcal mol-1, while the reaction to 4 endothermic by 1.3 kcal mol-1.

Interestingly, there is only one transition state that leads to both 3 and 4, the first known bispericyclic transition state for two conjoined [6+4] cycloadditions. The barrier is 27.9 kcal mol-1. The structures of the two products and the transition state leading to them are shown in Figure 1. 3 and 4 can interconvert through a Cope transition state, also shown in Figure 1, with a barrier of 26.3 kcal mol-1 (for 4 → 3).

3

4

TS [6+4]

TS Cope
Figure 1. B3LYP-D3/6-31G(d) optimized geometries.

Given that a single transition leads to two products, the product distribution is dependent on the molecular dynamics. A molecular dynamics simulation at B3LYP-D3/6-31G(d) with 117 trajectories indicates that 4 is formed 91% while 3 is formed only 9%. Once again, we are faced with the reality of much more complex reaction mechanisms/processes than simple models would suggest.


References

1) Yu, P.; Chen, T. Q.; Yang, Z.; He, C. Q.; Patel, A.; Lam, Y.-h.; Liu, C.-Y.; Houk, K. N., "Mechanisms and Origins of Periselectivity of the Ambimodal [6 + 4] Cycloadditions of Tropone to Dimethylfulvene." J. Am. Chem. Soc. 2017, 139 (24), 8251-8258, DOI: 10.1021/jacs.7b02966.


InChIs

1: InChI=1S/C7H6O/c8-7-5-3-1-2-4-6-7/h1-6H
InChIKey=QVWDCTQRORVHHT-UHFFFAOYSA-N
2: InChI=1S/C8H10/c1-7(2)8-5-3-4-6-8/h3-6H,1-2H3
InChIKey=WXACXMWYHXOSIX-UHFFFAOYSA-N
3:InChI=1S/C15H16O/c1-15(2)10-6-8-12(14(16)9-7-10)11-4-3-5-13(11)15/h3-12H,1-2H3
InChIKey=SEKRUGIZAIQCDA-UHFFFAOYSA-N
4: InChI=1S/C15H16O/c1-9(2)14-10-7-8-11(14)13-6-4-3-5-12(10)15(13)16/h3-8,10-13H,1-2H3
InChIKey=AQQAMUGJSGJKLC-UHFFFAOYSA-N


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Monday, July 31, 2017

A Robust and Accurate Tight-Binding Quantum Chemical Method for Structures, Vibrational Frequencies, and Noncovalent Interactions of Large Molecular Systems Parametrized for All spd-Block Elements (Z = 1−86)

Stefan Grimme, Christoph Bannwarth, and Philip Shushkov (2017)
Highlighted by Jan Jensen

Copyright American Chemical Society (2017)


Inspired by the success of the sTDA-xTB method for predicting electronic spectra, Grimme and co-workers present the tight binding DFT method GFN-xTB for predicting Geometries, vibrational Frequencies and Noncovalent interactions ("x" stands for extensions in the AO basis set and the form of the Hamiltonian).  

The method is implemented in a new program called xtb which includes shared memory parallelization, angeometry optimizer, MD, conformational searches, reaction path optimisation modules, and a GB solvation model supplemented with analytical nuclear gradients.  The software is free to academics and can be obtained by writing to the authors.

Importantly, the method is parameterized for all spd elements, so finally there is an alternative to PM6 for heavier elements. A Fermi smearing technique helps convergence for molecules with small HOMO-LUMO gaps and GFN-xTB gives structures organometallic compounds that are in good agreement with DFT.

As implied by the name, GFN-xTB is not parameterized against reaction energies but the methods gives reasonable results for properties like heats of formation.  However, as the authors write

A key premise of the present method is its special purpose character. In our view, low-cost semiempirical QM methods cannot describe simultaneously very different chemical proper- ties, such as structures and chemical reaction energies, and the GFN-xTB method (as the name conveys) focuses on structural properties. The efficiently computed structures and vibrational frequencies or the conformers obtained from global search procedures can (and should) be used subsequently for more accurate DFT or WFT refinements. We hope that the method can serve as a general tool in quantum chemistry and in particular recommend GFN-xTB optimized structures (and thermostatistical corrections) in a multilevel scheme together with PBEh-3c single-point energies. Large-scale molecular dynamics, screening of huge molecular spaces (libraries), parametrization of force-fields, or providing input for novel machine learning techniques are obvious other fields of application.
I thank Anders Christensen for bringing this paper to my attention


This work is licensed under a Creative Commons Attribution 4.0 International License.

Thursday, July 27, 2017

A few review articles

Contributed by Steven Bacharach
Reposted from Computational Organic Chemistry with permission

A few nice review/opinion pieces have been piling up in my folder of papers of interest for this Blog. So, this post provides a short summary of a number of review articles that computationally-oriented chemists may find of interest.


Holy Grails in computational chemistry

Houk and Liu present a short list of “Holy Grails” in computationally chemistry.1 They begin by pointing out a few technical innovations that must occur for the Grails to be found: development of a universal density functional; an accurate, generic force field; improved sampling for MD; and dealing with the combinatorial explosion with regards to conformations and configurations. Their list of Grails includes predicting crystal structures and structure of amorphous materials, catalyst design, reaction design, and device design. These Grails overlap with the challenges I laid out in my similarly-themed article in 2014.2


Post-transition state bifurcations and dynamics

Hare and Tantillo review the current understanding of post-transition state bifurcations (PTSB).3 This type of potential energy surface has been the subject of much of Chapter 8 of my book and many of my blog posts. What is becoming clear is the possibility of a transition state followed by a valley-ridge inflection leads to reaction dynamics where trajectories cross a single transition state but lead to two different products. This new review updates the state-of-the-art from Houk’s review4 of 2008 (see this post). Mentioned are a number of studies that I have included in this Blog, along with reactions involving metals, and biochemical systems (many of these examples come from the Tantillo lab). They close with the hope that their review might “inspire future studies aimed at controlling selectivity for reactions with PTSBs” (italics theirs). I might offer that controlling selectivity in these types of dynamical systems is another chemical Grail!
The Hase group has a long review of direct dynamics simulations.5 They describe a number of important dynamics studies that provide important new insight to reaction mechanism, such as bimolecular SN2 reactions (including the roundabout mechanism) and unimolecular dissociation. They write a long section on post-transition state bifurcations, and other dynamic effects that cannot be interpreted using transition state theory or RRKM. This section is a nice complement to the Tantillo review.


Benchmarking quantum chemical methods

Mata and Suhm look at our process of benchmarking computational methods.6 They point out the growing use of high-level quantum computations as the reference for benchmarking new methods, often with no mention of any comparison to experiment. In defense of theoreticians, they do note the paucity of useful experimental data that may exist for making suitable comparisons. They detail a long list of better practices that both experimentalists and theoreticians can take to bolster both efforts, leading to stronger computational tools that are more robust at helping to understand and discriminate difficult experimental findings.


References

1) Houk, K. N.; Liu, F., "Holy Grails for Computational Organic Chemistry and Biochemistry." Acc. Chem. Res. 2017, 50 (3), 539-543, DOI: 10.1021/acs.accounts.6b00532.
2) Bachrach, S. M., "Challenges in computational organic chemistry." WIRES: Comput. Mol. Sci. 2014, 4, 482-487, DOI: 10.1002/wcms.1185.
3) Hare, S. R.; Tantillo, D. J., "Post-transition state bifurcations gain momentum – current state of the field." Pure Appl. Chem. 2017, 89, 679-698, DOI: 0.1515/pac-2017-0104.
4) Ess, D. H.; Wheeler, S. E.; Iafe, R. G.; Xu, L.; Çelebi-Ölçüm, N.; Houk, K. N., "Bifurcations on Potential Energy Surfaces of Organic Reactions." Angew. Chem. Int. Ed. 2008, 47, 7592-7601, DOI: 10.1002/anie.200800918
5) Pratihar, S.; Ma, X.; Homayoon, Z.; Barnes, G. L.; Hase, W. L., "Direct Chemical Dynamics Simulations." J. Am. Chem. Soc. 2017, 139, 3570-3590, DOI: 10.1021/jacs.6b12017.
6) Mata, R. A.; Suhm, M. A., "Benchmarking Quantum Chemical Methods: Are We Heading in the Right Direction?" Angew. Chem. Int. Ed. 2017, ASAP, DOI: 10.1002/anie.201611308.


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Wednesday, July 26, 2017

Intrinsic map dynamics exploration for uncharted effective free-energy landscapes

Eliodoro Chiavazzo, Roberto Covino, Ronald R. Coifman, C. William Geard, Anastasia S. Georgiou, Gerhard Hummer, and Ioannis G. Kevrekidis. PNAS June 20, 2017
Contributed by Jesper Madsen

The desire to use enhancing sampling to save computational expense in simulations has been there from the beginning. Broadly speaking, there are two main approaches of enhancing molecular dynamics (MD) simulations in order to determine the free-energy surface (FES) of a computationally expensive Hamiltonian: 1) Trajectory-based enhanced sampling (e.g. temperature replica-exchange) and 2) collective variable (CV)-based methods (e.g. umbrella sampling). It is worth noting that the “zoo” of actual techniques to date is rather large. Either type of method, trajectory-based or CV-based, comes with its own set of advantages and disadvantages and mixing-and-matching is popular. 

Here I highlight a newcomer that is spun off from the rapidly growing field of Machine Learning – a field that most of us is keeping a keen eye on these days. 

The algorithm is called intrinsic map dynamics (iMapD) and it is conceptually simple (See Fig. 1 from the paper). 
1. Run a MD trajectory
2. Figure out, in some abstract sense, what region of configuration space you have sampled.
3. Determine the (non-linear) boundary of the sampled region in the abstract space
4. Initialize new MD trajectories in these boundary areas and explore the uncharted territories of the FES.

It is with the concretization of each step above that machine learning has contributed its ideas and tools. Specifically, the map of the configuration space is data mined and a d-dimensional manifold learning technique called diffusion maps (DMAPs) is applied to find the appropriate manifold and its dimensionality. The (d-1)-dimensional boundary manifold of the explored region is determined by a “wrapping” algorithm (here they use alpha shapes). Outward extrapolation is done by performing local principal component (LPC) analysis in ambient space. New simulations are seeded from these extended initial configurations and the algorithm loops back to the beginning. 


Fig.1: Pictorial illustration of the iMapD exploration procedure with (Left) 1D and (Right) 2D effective FESs. In Left Inset, a good collective coordinate is already available—the collective coordinates in Left and Right are not a priori known. "Copyright (2017) National Academy of Sciences

Since iMapD is a trajectory-based approach, absolutely no prior knowledge about the mechanistics of the process we study is required, which is appealing. One can think of the method as a clever hybrid MD/Monte Carlo algorithm and time will show how it stacks up against other alternative approaches in terms of practical usefulness. 

WInote that the algorithm is pedagogically presented in the paper and if you fancy Indiana Jones, well then there’s a little treat for you in the main text. Enjoy.

Wednesday, July 19, 2017

The Structure of the Elusive Simplest Dipeptide Gly-Gly

Cabezas, C.; Varela, M.; Alonso, J. L., Angew. Chem. Int. Ed. 2017, 56, 6420-6425
Contributed by Steven Bacharach
Reposted from Computational Organic Chemistry with permission

Continuing their application of laser ablation molecular beam Fourier transform microwave (LA-MB-FTMW) spectroscopy and computational chemistry to biochemical molecules (see these previous posts), the Alonso group reports on the structure of the glycine-glycine dipeptide 1.1 The microwave spectrum shows three different conformers. MP2/6-311++G(d,p) computations, the same method they have previously utilized for predicting geometries, revealed a number of different conformations. By matching the spectroscopic parameters obtained from the spectrum with those of the computed structures, they proposed the three conformations 1a1b, and 1c, shown in Figure 1.

1a

1b

1c
Figure 1. ωb97xd/6-31G(d) optimized structures of the three conformers of 1.
Note that the authors did not report their structures in their supporting materials(!) so I have optimized them.

The structures of conformers 1a and 1b are nearly planar. MP2 predicts a non-planar rotomer of 1a, which brings the carboxyl group out of plane, to be the lowest conformation in terms of electronic energy. With the M06-2x functional, this non-planar rotomer is about isoenergetic with 1a. With all computational levels 1a is the lowest in free energy. The barrier for rotation between the non-planar rotomer and 1a is very small, and this explains why it is not observed in the supersonic expansion.

References

1) Cabezas, C.; Varela, M.; Alonso, J. L., "The Structure of the Elusive Simplest Dipeptide Gly-Gly." Angew. Chem. Int. Ed. 2017, 56, 6420-6425, DOI: 10.1002/anie.201702425.

InChIs

1: InChI=1S/C4H8N2O3/c5-1-3(7)6-2-4(8)9/h1-2,5H2,(H,6,7)(H,8,9)
InChIKey=YMAWOPBAYDPSLA-UHFFFAOYSA-N


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Tuesday, July 18, 2017

Development of a 13C NMR Chemical Shift Prediction Procedure Using B3LYP/cc-pVDZ and Empirically Derived Systematic Error Correction Terms: A Computational Small Molecule Structure Elucidation Method

Xin, D.; Sader, C. A.; Chaudhary, O.; Jones, P.-J.; Wagner, K.; Tautermann, C. S.; Yang, Z.; Busacca, C. A.; Saraceno, R. A.; Fandrick, K. R.; Gonnella, N. C.; Horspool, K.; Hansen, G.; Senanayake, C. H., J. Org. Chem. 2017, ASAP
Contributed by Steven Bacharach
Reposted from Computational Organic Chemistry with permission


Here’s another take on automating a procedure for using computer 13C chemical shifts to assess chemical structure.1 (Have a look at these previous posts for some alternative methods and applications.) The approach here is to benchmark a few computational methods against a conformationally flexible drug-like molecule, in this case 1. A variety of conformations were optimized using the different computational methods, and 13C chemical shifts evaluated from a Boltzmann-weighted distribution. While the best agreement with the experimental chemical shifts (based on the root-mean-squared deviation) is with ωB97XD/cc-pVDZ, the authors opt for B3LYP/cc-pVDZ for its computational efficiency with only slightly poorer performance. (It should be note that WC04/cc-pVDZ, a functional designed for computing 13chemical shifts,2 is almost as good as ωB97XD/cc-pVDZ. Also, not mentioned in the article is the dramatically poorer performance of the pcS-2 basis set, despite the fact that it was parametrized3 for NMR computation!)
They apply the procedure to a number of test cases. For example, the HIV-1 reverse transcriptase inhibitor nevirapine hydrolyzes to a compound whose structure has been difficult to identify. The four proposed structures 2a-d were subjected to the computational method, and the 13C chemical shift RMSD for 2d is only 2.3ppm, significantly smaller than for the other 3 structures. Compound 2d was then synthesized and its NMR matches that of the nevirapine hydrolysis product.


References

1) Xin, D.; Sader, C. A.; Chaudhary, O.; Jones, P.-J.; Wagner, K.; Tautermann, C. S.; Yang, Z.; Busacca, C. A.; Saraceno, R. A.; Fandrick, K. R.; Gonnella, N. C.; Horspool, K.; Hansen, G.; Senanayake, C. H., "Development of a 13C NMR Chemical Shift Prediction Procedure Using B3LYP/cc-pVDZ and Empirically Derived Systematic Error Correction Terms: A Computational Small Molecule Structure Elucidation Method." J. Org. Chem. 2017, ASAP, DOI: 10.1021/acs.joc.7b00321.
2) Wiitala, K. W.; Hoye, T. R.; Cramer, C. J., “Hybrid Density Functional Methods Empirically Optimized for the Computation of 13C and 1H Chemical Shifts in Chloroform Solution,” J. Chem. Theory Comput. 20062, 1085-1092, DOI: 10.1021/ct6001016.
3) Jensen, F., “Basis Set Convergence of Nuclear Magnetic Shielding Constants Calculated by Density Functional Methods,” J. Chem. Theory Comput.20084, 719-727, DOI: 10.1021/ct800013z.


InChIs

1: InChI=1S/C24H26F4N2O4S/c1-4-35(33,34)18-7-8-20-15(10-18)9-17(30-20)13-23(32,24(26,27)28)22(2,3)12-14-5-6-16(25)11-19(14)21(29)31/h5-11,30,32H,4,12-13H2,1-3H3,(H2,29,31)/t23-/m0/s1
InChIKey=ILKZCEOVIFOUBJ-QHCPKHFHSA-N
2d: InChI=1S/C15H16N4O2/c1-9-6-8-17-14(18-10-4-5-10)12(9)19-13-11(15(20)21)3-2-7-16-13/h2-3,6-8,10H,4-5H2,1H3,(H,16,19)(H,17,18)(H,20,21)
InChIKey=ZLFOGBWAZNUXAD-UHFFFAOYSA-N

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

Monday, July 10, 2017

Does Proton Conduction in the Voltage-Gated H+ Channel hHv1 Involve Grotthuss-Like Hopping via Acidic Residues?

Siri C. van Keulen, Eleonora Gianti, Vincenzo Carnevale, Michael L. Klein, Ursula Rothlisberger, and Lucie Delemotte (2017)
Contributed by Dries Van Rompaey



Voltage gated proton channels are membrane channels that are are regulated by both the pH gradient and the voltage. In most cases these channels only open when the electrochemical proton gradient is outwards, functioning as a passive acid extrusion mechanism. They have an exquisite selectivity for protons, prompting Delemotte and coworkers to investigate the mechanism of proton transport and the origin of this selectivity.

The mechanism for proton transport was explored using an integrative approach, combining classical simulations with QM/MM. Three separate cation binding sites can be observed in the channel, each consisting of a pair of negatively charged residues. QM/MM simulations were used to examine binding and unbinding of the proton, while conformational rearrangements were sampled using classical simulation. In contrast to the classic Grotthus mechanism, where the proton is transported through a water wire by subsequent bond formation and bond breaking, simulations indicated that the proton may traverse the channel by jumping between the acidic residues, assisted by a water molecule. As shown by classical MD, structural rearrangements occur upon proton binding, orienting the residues for the next proton jump, allowing for the proton to move through the channel. This integrative modelling study provides an interesting and novel mechanism for the transfer of a proton across a membrane. It will be interesting to see where future work on this channel leads.

Sunday, June 25, 2017

A Deep Neural Network with Minimal Chemistry Knowledge Matches the Performance of Expert-developed QSAR/QSPR Models

Highlighted by Jan Jensen

Figure 1: The key difference in using deep learning algorithms as a machine learning tool as opposed to a “machine intelligence” tool is the assistance, augmentation and possible replacement, for human-led tasks like feature engineering in computational chemistry.

A lot of machine learning research in chemistry is focussed on finding the best descriptors for the property of interest.  This paper shows that simply using 2D images of molecules leads to similarly accurate predictions of solvation free energies, in vitro HIV activity, and in vivo toxicity. 

This seems to me an appropriate "null-model" that all machine learning studies should include. Another option would be SMILES strings or some representation thereof. If your fancy descriptor doesn't lead to significantly better predictions then it's back to the drawing board.

The manuscript doesn't mention code availability but one of the co-authors tells me that they plan to make to code available when it is ready.

Saturday, June 24, 2017

London Dispersion Enables the Shortest Intermolecular Hydrocarbon H···H Contact.

Rösel, S.; Quanz, H.; Logemann, C.; Becker, J.; Mossou, E.; Cañadillas-Delgado, L.; Caldeweyher, E.; Grimme, S.; Schreiner, P. R.,  J. Am. Chem. Soc. 2017, 139, 7428–7431
Contributed by Steven Bacharach
Reposted from Computational Organic Chemistry with permission

Following on previous work (see these posts on ladderane and hexaphenylethane), Schreiner, Grimme and co-workers have examined the structure of the all-meta tri(di-t-butylphenyl)methane dimer 12.1 In the study of hexaphenylethane,2 Schreiner and Grimme note that t-butyl groups stabilize highly congested structures through dispersion, identifying them as “dispersion energy donors”.3 The idea here is that the dimer of 1 will be stabilized by these many t-butyl groups. In fact, the neutron diffraction study of the crystal structure of 12 shows an extremely close approach of the two methane hydrogens of only 1.566 Å, the record holder for the closest approach of two formally non-bonding hydrogen atoms.


To understand the nature of this dimeric structure, they employed a variety of computational techniques. (Shown in Figure 1 is the B3LYPD3ATM(BJ)/def2-TZVPP optimized geometry of 12.) The HSE-3c (a DFT composite method) optimized crystal structure predicts the HH distance is 1.555 Å. The computed gas phase structure lengthens the distance to 1.634 Å, indicating a small, but essential, role for packing forces. Energy decomposition analysis of 12 at B3LYP-D3ATM(BJ)/def2-TZVPP indicates a dominant role for dispersion in holding the dimer together. While 12 is bound by about 8 kcal mol-1, the analogue of 12lacking all of the t-butyl groups (the dimer of triphenylmethane 22) is unbound by over 8 kcal mol-1. Topological electron density analysis does show a bond critical point between the two formally unbound hydrogen atoms, and the noncovalent interaction plot shows an attractive region between these two atoms.

Figure 1ATM(BJ)/def2-TZVPP optimized geometry of 12, with most of the hydrogens suppressed for clarity. (Selecting the molecule will launch Jmol with the full structure, including the hydrogens.)


References

1) Rösel, S.; Quanz, H.; Logemann, C.; Becker, J.; Mossou, E.; Cañadillas-Delgado, L.; Caldeweyher, E.; Grimme, S.; Schreiner, P. R., "London Dispersion Enables the Shortest Intermolecular Hydrocarbon H···H Contact." J. Am. Chem. Soc. 2017139, 7428–7431, DOI: 10.1021/jacs.7b01879.
2) Grimme, S.; Schreiner, P. R., "Steric Crowding Can Stabilize a Labile Molecule: Solving the Hexaphenylethane Riddle." Angew. Chem. Int. Ed. 2011, 50 (52), 12639-12642, DOI: 10.1002/anie.201103615.
3) Grimme, S.; Huenerbein, R.; Ehrlich, S., "On the Importance of the Dispersion Energy for the Thermodynamic Stability of Molecules." ChemPhysChem 2011, 12 (7), 1258-1261, DOI: 10.1002/cphc.201100127.


InChIs

1: InChI=1S/C43H64/c1-38(2,3)31-19-28(20-32(25-31)39(4,5)6)37(29-21-33(40(7,8)9)26-34(22-29)41(10,11)12)30-23-35(42(13,14)15)27-36(24-30)43(16,17)18/h19-27,37H,1-18H3
InChIKey=VFNQDWKFTWSJAU-UHFFFAOYSA-N

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

Saturday, June 10, 2017

Dynamic Effects Responsible for High Selectivity in a [3,3] Sigmatropic Rearrangement Featuring a Bispericyclic Transition State

Villar López, R.; Faza, O. N.; Silva López, C., J. Org. Chem. 2017, 82 (9), 4758-4765
Contributed by Steven Bacharach
Reposted from Computational Organic Chemistry with permission

Bispericyclic reactions occur when two different pericyclic reactions merge to have a single transition state. An example of this is the joining of two [3,3]-sigmatopic rearrangements of 1 that merge to have a single transition state. Lopez, Faza, and Lopez have examined the dynamics of this reaction.1


Because of the symmetry of the species along this reaction pathway, the products of the two different rearrangements are identical, and will be formed in equal amounts, though they are produced from a single transition state with the reaction pathway bifurcating due to a valley-ridge inflection post TS.

The interesting twist that is explored here is when 1 is substituted in order to break the symmetry. The authors have examined 3x with either fluorine, chlorine, or bromine. The critical points on the reactions surface were optimized at M06-2X/Def2TZVPP. In all three cases a single bispericyclic transition state 3TS1x is found, which leads to products 4a and 4b. A second transition state 4TSx corresponds to the [3,3]-rearrangement that interconverts the two products. The structures of 1TS3TS1F, and 3TS1Cl are shown in Figure 1.

1TS

3TS1F

3TS1Cl
Figure 1. M06-2X/Def2TZVPP optimized geometries of 1TS3TS1F, and 3TS1Cl.

The halogen substitution breaks the symmetry of the reaction path. This leads to a number of important changes. First, the C4-C5 and C7-C8 distances, which are identical in 1TS, are different in the halogen cases. Interestingly, the distortions are dependent on the halogen: in 3TS1F C4-C5 is 0.2 Å longer than C7-C8, but in 3TS1Cl C7-C8 is much longer (by 0.65 Å) than C4-C5. Second, the products are no longer equivalent with the halogen substitution. Again, this is halogen dependent: 4bF is 4.0 kcal mol-1 lower in energy than 4aF, while 4aCl is 8.2 kcal mol-1 lower than 4bCl.

These difference manifest in very different reaction dynamics. With trajectories initiated at the first (bispericyclic) transiting state, 89% end at 4bF and 9% end at 4aF, a ratio far from unity that might be expected from both products resulting from passage through the same TS. The situation is even more extreme for the chlorine case, where all 200 trajectories end in 4aCl. This is yet another example of the role that dynamics play in reaction outcomes (see these many previous posts).


References

1) Villar López, R.; Faza, O. N.; Silva López, C., "Dynamic Effects Responsible for High Selectivity in a [3,3] Sigmatropic Rearrangement Featuring a Bispericyclic Transition State." J. Org. Chem. 2017, 82 (9), 4758-4765, DOI: 10.1021/acs.joc.7b00425.


InChIs

1: InChI=1S/C9H12/c1-3-9-6-4-8(2)5-7-9/h1-2,4-7H2
InChIKey=RRXCPJIEZVQPSZ-UHFFFAOYSA-N
2: InChI<=1S/C9H12/c1-7-4-5-8(2)9(3)6-7/h1-6H2
InChIKey=AMBNQWVPTPHADI-UHFFFAOYSA-N
3F: InChI=1S/C9H8F4/c1-3-7-5-4-6(2)8(10,11)9(7,12)13/h1-2,4-5H2
InChIKey=VZFAQFJKHDWJDN-UHFFFAOYSA-N
3Cl: InChI=1S/C9H8Cl4/c1-3-7-5-4-6(2)8(10,11)9(7,12)13/h1-2,4-5H2
InChIKey=AIVUHFMHIMNOJB-UHFFFAOYSA-N
4aF: InChI=1S/C9H8F4/c1-5-4-6(8(10)11)2-3-7(5)9(12)13/h1-4H2
InChIKey=NAUUHIHYMAOMIF-UHFFFAOYSA-N
4aCl: InChI=1S/C9H8Cl4/c1-5-4-6(8(10)11)2-3-7(5)9(12)13/h1-4H2
InChIKey=MMCKDJXQYSGQEH-UHFFFAOYSA-N
4bF: InChI=1S/C9H8F4/c1-5-4-6(2)8(10,11)9(12,13)7(5)3/h1-4H2
InChIKey=LMFNAIRCNARWSX-UHFFFAOYSA-N
4bCl: InChI=1S/C9H8Cl4/c1-5-4-6(2)8(10,11)9(12,13)7(5)3/h1-4H2
InChIKey=NOFFASDSCUGRTP-UHFFFAOYSA-N

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Saturday, May 27, 2017

Synthesis of a carbon nanobelt

Povie, G.; Segawa, Y.; Nishihara, T.; Miyauchi, Y.; Itami, K. Science 2017, 356, 172-175
Contributed by Steven Bacharach
Reposted from Computational Organic Chemistry with permission

The synthesis of components of nanostructures (like fullerenes and nanotubes) has dramatically matured over the past few years. I have blogged about nanohoops before, and this post presents the recent work of the Itami group in preparing the nanobelt 1.1

1

The synthesis is accomplished through a series of Wittig reactions with an aryl-aryl coupling to stitch together the final rings. The molecule is characterized by NMR and x-ray crystallography. The authors have also computed the structure of 1 at B3LYP/6-31G(d), shown in Figure 1. The computed C-C distances match up very well with the experimental distances. The strain energy of 1, presumably estimated by Reaction 1,2 is computed to be about 119 kcal mol-1.

1
Figure 1. B3LYP/6-31G(d) optimized structure of 1.
Rxn 1
NICS(0) values were obtained at B3LYP/6-311+G(2d,p)//B3LYP/6-31G(d); the rings along the middle of the belt have values of -7.44ppm and are indicative of normal aromatic 6-member rings, while the other rings have values of -2.00ppm. This suggests the dominant resonance structure shown below:

References

1) Povie, G.; Segawa, Y.; Nishihara, T.; Miyauchi, Y.; Itami, K., "Synthesis of a carbon nanobelt." Science 2017, 356, 172-175, DOI: 10.1126/science.aam8158.
2) Segawa, Y.; Yagi, A.; Ito, H.; Itami, K., "A Theoretical Study on the Strain Energy of Carbon Nanobelts." Org. Letters 2016, 18, 1430-1433, DOI: 10.1021/acs.orglett.6b00365.

InChIs:

1: InChI=1S/C48H24/c1-2-26-14-40-28-5-6-31-20-44-32(19-42(31)40)9-10-34-24-48-36(23-46(34)44)12-11-35-21-45-33(22-47(35)48)8-7-30-17-41-29(18-43(30)45)4-3-27-15-37(39(26)16-28)25(1)13-38(27)41/h1-24H
InChIKey=KJWRWEMHJRCQKK-UHFFFAOYSA-N



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Sunday, May 21, 2017

Solving the Density Functional Conundrum: Elimination of Systematic Errors To Derive Accurate Reaction Enthalpies of Complex Organic Reactions


Highlighted by Jan Jensen



Sengupta and Raghavachari present a quick and efficient way to increase the accuracy of computed reaction energies (ΔE).  For example, it is difficult to compute the reaction energy for Rxn1 because the bonding changes a lot: in effect, two double bonds are changed to 4 single bonds. By the same logic, it should be much easier to compute an accurate reaction energy for Rxn2.  

ΔE(Rxn1) = ΔE(Rxn2) - ΔE(Rxn3) 

So one should be able to get a good estimate of ΔE(Rxn1) by computing ΔE(Rxn2) and ΔE(Rxn3) at a relatively low and high level of theory, respectively. The accuracy can be further increased by larger fragments, either in Rxn3 or in an additional reaction.

Sengupta and Raghavachari test a four-reaction approach for 25 different reactions and a large variety of methods (DFT, HF, MP2, and CCSD(T)) and show that the mean absolute error relative to G4 can be reduced to ca 2 kcal/mol or less using the 6-311++G(3df,2p) basis set. For M06-2X they also tested the effect of basis set and showed that the MAE only increases from 2.2 to 2.6 kcal/mol on to the 6-31G(d) basis set.

Of course the high level calculations on the small fragments only have to be done once and a relatively small number of different fragments will be needed to cover most organic reactions.

Wednesday, May 10, 2017

Progress in DFT development and the density they predict

Medvedev, M. G.; Bushmarinov, I. S.; Sun, J.; Perdew, J. P.; Lyssenko, K. A., "Density functional theory is straying from the path toward the exact functional." Science 2017, 355, 49-52
Hammes-Schiffer, S., "A conundrum for density functional theory." Science 2017, 355, 28-29
Korth, M., "Density Functional Theory: Not Quite the Right Answer for the Right Reason Yet." Angew. Chem. Int. Ed. 2017, 56, 5396-5398
Contributed by Steven Bacharach
Reposted from Computational Organic Chemistry with permission

“Getting the right answer for the right reason” – how important is this principle when it comes to computational chemistry? Medvedev and co-workers argue that when it comes to DFT, trends in functional development have overlooked this maxim in favor of utility.1 Specifically, they note that
There exists an exact functional that yields the exact energy of a system from its exact density.
Over the past two decades a great deal of effort has gone into functional development, mostly in an empirical way done usually to improve energy prediction. This approach has a problem:
[It], however, overlooks the fact that the reproduction of exact energy is not a feature of the exact functional, unless the input electron density is exact as well.
So, these authors have studied functional performance with regards to obtaining proper electron densities. Using CCSD/aug-cc-pwCV5Z as the benchmark, they computed the electron density for a number of neutral and cationic atoms having 2, 4, or 10 electrons. Then, they computed the densities with 128 different functionals of all of the rungs of Jacob’s ladder. They find that accuracy was increasing as new functionals were developed from the 1970s to the early 2000s. Since then, however, newer functionals have tended towards poorer electron densities, even though energy prediction has continued to improve. Medvedev et al argue that the recent trend in DFT development has been towards functionals that are highly parameterized to fit energies with no consideration given to other aspects including the density or constraints of the exact functional.

In the same issue of Science, Hammes-Schiffer comments about this paper.2 She notes some technical issues, most importantly that the benchmark study is for atoms and that molecular densities might be a different issue. But more philosophically (and practically), she points out that for many chemical and biological systems, the energy and structure are of more interest than the density. Depending on where the errors in density occur, these errors may not be of particular relevance in understanding reactivity; i.e., if the errors are largely near the nuclei but the valence region is well described then reactions (transition states) might be treated reasonably well. She proposes that future development of functionals, likely still to be driven by empirical fitting, might include other data to fit to that may better reflect the density, such as dipole moments. This seems like a quite logical and rational step to take next.
A commentary by Korth3 summarizes a number of additional concerns regarding the Medvedev paper. The last concern is the one I find most striking:
Even if there really are (new) problems, it is as unclear as before how they can be overcome…With this in mind, it does not seem unreasonable to compromise on the quality of the atomic densities to improve the description of more relevant properties, such as the energetics of molecules.
Korth concludes with
In the meantime, while theoreticians should not rest until they have the right answer for the right reason, computational chemists and experimentalists will most likely continue to be happy with helpful answers for good reasons.
I do really think this is the correct take-away message: DFT does appear to provide good predictions of a variety of chemical and physical properties, and it will remain a widely utilized tool even if the density that underpins the theory is incorrect. Functional development must continue, and Medvedev et al. remind us of this need.


References

1) Medvedev, M. G.; Bushmarinov, I. S.; Sun, J.; Perdew, J. P.; Lyssenko, K. A., "Density functional theory is straying from the path toward the exact functional." Science 2017, 355, 49-52, DOI: 10.1126/science.aah5975.
2) Hammes-Schiffer, S., "A conundrum for density functional theory." Science 2017, 355, 28-29, DOI: 10.1126/science.aal3442.
3) Korth, M., "Density Functional Theory: Not Quite the Right Answer for the Right Reason Yet." Angew. Chem. Int. Ed. 201756, 5396-5398, DOI: 10.1002/anie.201701894.


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Tuesday, May 9, 2017

Puckering Energetics and Optical Activities of [7]Circulene Conformers

Hatanaka, M., J. Phys. Chem. A 2016, 120 (7), 1074-1083
Contributed by Steven Bacharach
Reposted from Computational Organic Chemistry with permission

I have discussed the circulenes in a few previous posts. Depending on their size, they can be bowls, flat disks, or saddles. A computational study of [7]circulene noted that C2 structure is slightly higher in energy than the Cs form,1 though the C2 form is found in the x-ray structure.2
Now, Miao and co-workers have synthesized the tetrabenzo[7]circulene 1 and also examined its structure using DFT.3


As with the parent compound, a C2 and Cs form were located at B3LYP/6-31G(d,p), and are shown in Figure 1. The C2 form is 7.6 kcal mol-1 lower in energy than the Cs structure, and the two are separated by a transition state (also shown in Figure 1) with a barrier of 12.2 kcal mol-1. The interconversion of these conformations takes place without going through a planar form. The x-ray structure contains only the C2structure. It should be noted that the C2 structure is chiral, and racemization would take place by the path: 1-Cs ⇆ 1-Cs ⇆ 1-C2*, where 1-C2* is the enantiomer of 1-C2.

1-C2

1-TS

1-Cs
Figure 1. B3LYP/6-31G(d,p) optimized structures of 1.


References

1) Hatanaka, M., "Puckering Energetics and Optical Activities of [7]Circulene Conformers." J. Phys. Chem. A 2016, 120 (7), 1074-1083, DOI: 10.1021/acs.jpca.5b10543.
2) Yamamoto, K.; Harada, T.; Okamoto, Y.; Chikamatsu, H.; Nakazaki, M.; Kai, Y.; Nakao, T.; Tanaka, M.; Harada, S.; Kasai, N., "Synthesis and molecular structure of [7]circulene." J. Am. Chem. Soc. 1988, 110 (11), 3578-3584, DOI: 10.1021/ja00219a036.
3) Gu, X.; Li, H.; Shan, B.; Liu, Z.; Miao, Q., "Synthesis, Structure, and Properties of Tetrabenzo[7]circulene." Org. Letters 2017, DOI: 10.1021/acs.orglett.7b00714.


InChIs

1: InChI=1S/C44H22/c1-5-13-28-24(9-1)32-19-17-23-18-20-33-25-10-2-6-14-29(25)38-31-16-8-4-12-27(31)35-22-21-34-26-11-3-7-15-30(26)37(28)43-39(32)36(23)40(33)44(38)42(35)41(34)43/h1-22H
InChIKey=KVMXYGAVHDZMNP-UHFFFAOYSA-N