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

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

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

Friday, May 5, 2017

Empirical D3 dispersion as a replacement for ab initio dispersion terms in density functional theory-based symmetry-adapted perturbation theory

Robert Sedlak and Jan Řezáč (2017)
Highlighted by Amelia Fitzsimmons


Sedlak and Rezac presented an approximation to DFT-SAPT that replaces the ab initio dispersion terms in the popular but expensive SAPT calculation with a potential that is based on Grimme’s D3 dispersion. The D3 dispersion correction has become a popular way to improve the accuracy of DFT geometry optimizations and thermochemistry calculations for systems involving noncovalent interactions, and as applied to DFT-SAPT improves the efficiency of DFT-SAPT calculations. They demonstrated with the S66X8 and S66X6 test sets that this correction has root mean square errors of less than 1 kcal/mol for non-charge transfer species. I think this could be useful to anyone who is looking for a more efficient way to do energy decomposition analysis who has previously used DFT-SAPT, or anyone who is interested in noncovalent interactions and dispersion corrections to DFT. 

Saturday, April 29, 2017

Cheap but accurate calculation of chemical reaction rate constants from ab initio data, via system-specific, black-box force fields

Julien Steffen and Bernd Hartke 2017
Highlighted by Jan Jensen

Figure 1 from the paper. A flowchart of the EVB-QMDFF program implemented in this work, for the case of a DG-EVB-QMDFF calculation.


A few years ago I highlighted Grimme's General Quantum Mechanically Derived Force Field (QMDFF) - a black box approach that gives you a system-specific force field from a single QM Hessian calculation.  I missed the fact that Hartke and Grimme extended this approach to TSs using EVB, a year later. This EVB-QMDFF approach constructs EVB potentials connecting each pair of minima described by QMDFF.  To get the EVB parameters you need to supply the TS and 10-100 energies (and possibly 5-10 Hessian calculations) along the reaction path, depending on how complex an EVB potential is needed to describe the reaction.

What's the point of a system-specific reactive force field when you already have the TS and reaction path? Well, Steffen and Hartke show is that EVB-QMDFF can be used to perform the additional calculations needed for, for example, variational TS theory or ring polymer MD calculations to get more accurate rate constants.

Furthermore, just like for QMDFF for minima you could do all this for one conformation of ligands and use EVB-QMDFF for a conformer search or use the gas phase parameterized model to study the effect of explicit solvation.  It might even be possible to parameterize EVB-QMDFF using small ligands and then model the effect of larger ligands using the QMDFF parameters obtained for the minima.  However, all these potential uses still need to be tested.

I thank Jean-Philip Piquemal for bringing this paper to my attention



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

Friday, April 28, 2017

Local Fitting of the Kohn−Sham Density in a Gaussian and Plane Waves Scheme for Large-Scale Density Functional Theory Simulations

Dorothea Golze, Marcella Iannuzzi, and Jürg Hutter (2017)
Highlighted by Michael Banck

Reprinted (adapted) with permission from Dorothea Golze, Marcella Iannuzzi, and Jürg Hutter. Journal of Chemical Theory and Computation, 2017 ASAP,  Copyright 2017 American Chemical Society.

Hutter et al. have published their LRIGPW (local resolution-of-the-identity gaussian and plane waves method) paper in JCTC. The image above taken from that paper highlights that much of the total runtime for conventional GPW (the main method implemented in the CP2K package) is spent on the description of the total charge density on real-space grids ("GPW grid", dark blue). Can you spot the orange bars for the same work done in the LRIGPW approach? This takes CP2K another big step forward.