Tuesday, September 18, 2018

Rearrangement of Hydroxylated Pinene Derivatives to Fenchone-Type Frameworks: Computational Evidence for Dynamically-Controlled Selectivity

Blümel, M.; Nagasawa, S.; Blackford, K.; Hare, S. R.; Tantillo, D. J.; Sarpong, R., J. Am. Chem. Soc. 2018, 140, 9291-9298
Contributed by Steven Bacharach
Reposted from Computational Organic Chemistry with permission

Sarpong and Tantillo have examined the acid-catalyzed Prins/semipinacol rearrangement of hydroxylated pinenes, such as Reaction 1.1
Rxn 1
Interestingly, only the fenchone scaffold products, like 1, are observed and the camphor scaffold products, like 2, are not observed. Cation intermediates are likely, and this means that a primary alkyl shift is taking place in preference to a tertiary alkyl shift, see Scheme 1.

Scheme 1.

Primary alkyl shift

Tertiary alkyl shift

They proposed the following key steps in the reaction mechanism:

ωB97X-D/6-31+G(d,p) computations find a flat surface around cation intermediate 4: the TS leading to 5and 6 are only 1.3 and 3.3 kcal mol-1, respectively. Since these small barriers are quite susceptible to changes in basis set and functional, and since Tantillo has found many examples of post-transition state bifurcations in cation systems, the authors reasonably decided to conduct molecular dynamics trajectories originating at the TS connecting 3 and 4. The geometries of the critical points are shown in Figure 1.

The trajectory study shows all the usual characteristics of reactions that are under dynamic control. A third of the trajectories show recrossing of the barrier, typical of very flat surfaces. Nearly all of the remaining trajectories led to 5, with only 2 trajectories (~1%) leading to 6. The dynamics are understandable in terms of favoring the primary alkyl shift over the tertiary since a significantly smaller mass needs to move in the former case.


TS 3 → 4

4

TS 4 → 5

TS 4 → 6
Figure 1. ωB97X-D/6-31+G(d,p) optimized geometries.

This is yet another study that implicates dynamic effects in routine reactions, one of many I have discussed over the years.

References

1. Blümel, M.; Nagasawa, S.; Blackford, K.; Hare, S. R.; Tantillo, D. J.; Sarpong, R., "Rearrangement of Hydroxylated Pinene Derivatives to Fenchone-Type Frameworks: Computational Evidence for Dynamically-Controlled Selectivity." J. Am. Chem. Soc. 2018140, 9291-9298, DOI: 10.1021/jacs.8b05804.

InChIs

1: InChI=1S/C17H20O2/c1-16-9-12-8-13(16)14(11-6-4-3-5-7-11)19-10-17(12,2)15(16)18/h3-7,12-14H,8-10H2,1-2H3/t12?,13?,14-,16?,17?/m0/s1
InChIKey=LTTUIPPXEHHMJS-XWTIBIIYSA-N
2: InChI=1S/C17H20O2/c1-16-10-19-15(11-6-4-3-5-7-11)13-8-12(16)9-14(18)17(13,16)2/h3-7,12-13,15H,8-10H2,1-2H3/t12?,13?,15-,16?,17?/m0/s1
InChIKey=GCKIOHNLJYVWKL-CMESGNGWSA-N


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Wednesday, August 29, 2018

A Density Functional Tight Binding Layer for Deep Learning of Chemical Hamiltonians

Haichen Li, Christopher Collins, Matteus Tanha, Geoffrey J. Gordon, David J. Yaron (2018)
Highlighted by Jan Jensen


There are increasingly many papers on predicting the molecular energy and other properties using machine learning (ML). Most, if not all, use some similarity measure of the molecular structure to structures in the training set when training. This paper uses DFTB Hamiltonian matrix elements instead and treats the short-range matrix elements as adjustable parameters (weights) to be trained. To make this happen, DFTB is implemented as a layer for deep learning, using the TensorFlow deep learning framework, by recasting the DFTB equations in terms of tensor operations. In this way domain knowledge is incorporated into the ML model. Since the starting values are the "conventional" DFTB parameters one can also view this as refining the DFTB method.

This DFTB-ML approach is evaluated on 15,700 hydrocarbons by comparing the RMSE in energy per heavy atom (Eatom) relative to ωB97X/6-31G(d) reference values. Training on up to 7 heavy atoms and testing on 8 heavy atoms, leads to RMS errors in Eatom of 0.72 kcal/mol, compared to 1.80 using conventional DFTB. Training on up to 4 heavy atoms gives an Eatom RMSE of 1.08 kcal/mol. The results can be further improved by using neural networks to allow the matrix elements to depend on the molecular environment of the atoms.

As the authors point out the performance on the training data remained above chemical accuracy (0.5 kcal/mol) for the total molecular energy, but they offer several interesting ideas on how to improve the performance.


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Nano-Saturn: Experimental Evidence of Complex Formation of an Anthracene Cyclic Ring with C60

Yuta, Y.; Eiji, T.; Kan, W.; Shinji, T., Angew. Chem. Int. Ed. 2018, 57, 8199-8202
Contributed by Steven Bacharach
Reposted from Computational Organic Chemistry with permission

It never hurts to promote one’s science through clever names – think cubane, buckminsterfullerene, bullvalene, etc. Host-guest chemistry is not immune to this cliché too, and this post discusses the latest synthesis and computations of a nano-Saturn; nano-Saturns are a spherical guest molecule captured inside a ring host molecule. I discussed an example of this a number of years ago – the nano-Saturn comprised of C60 fullerene surrounded by [10]cycloparaphenylene.

Yamamoto, Tsurumaki, Wakamatsu, and Toyota have prepared a nano-Saturn complex with the goal of making a flatter ring component.1 The inner planet is modeled again by C60 and the ring is the [24]circulene analogue 1. The x-ray crystal structure of this nano-Saturn complex is shown in Figure 1.

1: R = 2,4,6-tri-iso-propylphenyl
2: R = H
Figure 1. X-ray crystal structure of the nano-Saturn complex of 1 with C60.

Variable temperature NMR experiments gave the binding values of ΔH = -18.1 ± 2.3 kJ mol-1 and TΔS = 0.8 ± 2.2 kJ mol-1 at 298 K. To gauge this binding energy, they computed the complex of C60 with the parent compound 2 at B3LYP/6-1G(d)//M05-2X/6-31G(d), unfortunately without publishing the coordinates in the supporting materials. The computed binding enthalpy is ΔH = -50.6 kJ mol-1, but this computation is for the gas phase. The computed structure shows close contacts of 0.29 nm between the fullerene and the C9-proton of the anthracenyl groups, in excellent agreement with the x-ray structure. These weak C-Hπ interactions undoubtedly help stabilize the complex, especially given that the fullerene carries a very tiny Mulliken charge of +0.08 e.

References

1) Yuta, Y.; Eiji, T.; Kan, W.; Shinji, T., "Nano-Saturn: Experimental Evidence of Complex Formation of an Anthracene Cyclic Ring with C60." Angew. Chem. Int. Ed. 2018, 57, 8199-8202, DOI: 10.1002/anie.201804430.

InChIs

1: InChI=1S/C174H180/c1-91(2)121-78-150(97(13)14)164(151(79-121)98(15)16)163-90-128-71-139-70-127-59-109(37-38-120(127)77-162(139)163)110-39-49-140-129(60-110)72-130-61-111(40-50-141(130)165(140)170-152(99(17)18)80-122(92(3)4)81-153(170)100(19)20)112-41-51-142-131(62-112)73-132-63-113(42-52-143(132)166(142)171-154(101(21)22)82-123(93(5)6)83-155(171)102(23)24)114-43-53-144-133(64-114)74-134-65-115(44-54-145(134)167(144)172-156(103(25)26)84-124(94(7)8)85-157(172)104(27)28)116-45-55-146-135(66-116)75-136-67-117(46-56-147(136)168(146)173-158(105(29)30)86-125(95(9)10)87-159(173)106(31)32)118-47-57-148-137(68-118)76-138-69-119(128)48-58-149(138)169(148)174-160(107(33)34)88-126(96(11)12)89-161(174)108(35)36/h37-108H,1-36H3
InChIKey=AMDNULXMAMDTMX-UHFFFAOYSA-N
2: InChI=1S/C84H48/c1-13-61-25-62-15-3-51-33-75(62)43-73(61)31-49(1)50-2-14-63-26-64-16-4-52(34-76(64)44-74(63)32-50)54-6-18-66-28-68-20-8-56(38-80(68)46-78(66)36-54)58-10-22-70-30-72-24-12-60(42-84(72)48-82(70)40-58)59-11-23-71-29-69-21-9-57(39-81(69)47-83(71)41-59)55-7-19-67-27-65-17-5-53(51)35-77(65)45-79(67)37-55/h1-48H
InChIKey=ZYXXLAYETADMDM-UHFFFAOYSA-N


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Thursday, August 16, 2018

Readily Accessible Ambiphilic Cyclopentadienes for Bioorthogonal Labeling

Levandowski, B. J.; Gamache, R. F.; Murphy, J. M.; Houk, K. N., J. Am. Chem. Soc. 2018, 140, 6426-6431
Contributed by Steven Bacharach
Reposted from Computational Organic Chemistry with permission

I recently posted on a paper proposing 1,2-benzoquinone and related compounds as the diene component for bioorthogonal labeling. Levandowski, Gamache, Murphy, and Houk report on tetrachlorocyclopentadiene ketal 1 as an active ambiphilic diene component.1
1 is sterically congested to diminish self-dimerization and will react with both electron-rich and electron-poor dienes. To test it as an active diene in bioorthogonal labeling applications, they optimized the structures of the transition states at CPCM(water)/M06-2X/6-311+G(d,p)//CPCM(water)/M06-2X/6-31G(d) for the Diels-Alder reaction of 1 with a variety of dienophiles including trans-cyclooctene 2 and endo-bicyclononyne 3. These transition states are shown in Figure 1. The activation free energy is quite low for each: 18.1 kcal mol-1 with 2 and 18.9 kcal mol-1 with 3.

TS(1+2)

TS(1+3)
Figure 1. CPCM(water)/M06-2X/6-31G(d) optimized geometries for the TSs of the reaction of 1 with 2and 3.

Experiments were successfully run using 1 as a label on a neuropeptide.

References

1) Levandowski, B. J.; Gamache, R. F.; Murphy, J. M.; Houk, K. N., "Readily Accessible Ambiphilic Cyclopentadienes for Bioorthogonal Labeling." J. Am. Chem. Soc. 2018140, 6426-6431, DOI: 10.1021/jacs.8b02978.

InChIs

1:InChI=1S/C7H4Cl4O2/c8-3-4(9)6(11)7(5(3)10)12-1-2-13-7/h1-2H2
InChIkey=DXQQKKGWMVTLOJ-UHFFFAOYSA-N



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Sunday, July 29, 2018

Error-Controlled Exploration of Chemical Reaction Networks with Gaussian Processes

Gregor N. Simm and Markus Reiher (2018)
Highlighted by Jan Jensen



See if you recognise this scenario: you benchmark a cheap method against an accurate, but expensive, method for a set of molecules and get a mean error that you can use to correct the results obtained with the cheap method. But sooner or later you  start using the cheap method on molecules that look increasingly different from your benchmark set. At what point should you do another benchmark calculation against your expensive method? Simm and Reiher use Gaussian Processes (GP) to provide a quantitative answer.

GP is a way to fit a numerical function to a set of data points with uncertainties of the fit for every point in the fit. Simm and Reiher's basic idea is to arrange the benchmark points on the x-axis by computing a distance between pairs of molecular structures and performing a GP fit. Now compute the x-coordinate of the molecule you're uncertain about: if the uncertainty in the GP fit for that point is larger than the standard deviation of the mean error of your cheap method computed for the benchmark set, then you need to benchmark your cheap method against the more expensive method for that molecule.

Wednesday, July 25, 2018

Spectroscopic Evidence for Aminomethylene (H−C̈−NH2)—The Simplest Amino Carbene

Eckhardt, A. K.; Schreiner, P. R., Angew. Chem. Int. Ed. 2018, 57, 5248-5252
Contributed by Steven Bacharach
Reposted from Computational Organic Chemistry with permission

Eckhardt and Schreiner have spectroscopically characterized the aminomethylene carbene 1.1 Their characterization rests on IR spectra, with comparison to the computed AE-CCSD(T)/cc-pCVQZ anharmonic vibrational frequencies, and the UV-Vis spectra, with comparison to the computed B3LYP/6–311++G(2d,2p) transitions.


1 can be converted to 2 by photolysis. Interestingly, 1 does not convert to 2 after 5 days on the matrix in the dark. This is in distinct contrast to hydroxycarbene and related other carbene which undergo quantum mechanical tunneling (see this post and this post). Examination of the potential energy surface for the reaction of 1 to 2 at AE-CCSD(T)/cc-pCVQZ (see Figure 1) identifies that the lowest barrier is 45.8 kcal mol-1, about 15 kcal mol-1 larger than the barrier for the hydroxycarbene rearrangement. Additionally, the barrier width for 1 → 2 is 25% larger than for the hydroxycarbenes. Both of these suggest substantially reduced tunneling, and WKB analysis predicts a tunneling half-life of more than a billion years. The stability of 1 is attributed to the strong π-donor ability of nitrogen to the electron-poor carbene. This is reflected in a very short C-N bond (1.27 Å).

Figure 1. Structures and energies of 1 and 2 and the transition states that connect them. The relative energies (kcal mol-1) are computed at AE-CCSD(T)/cc-pCVQZ.


References

1) Eckhardt, A. K.; Schreiner, P. R., "Spectroscopic Evidence for Aminomethylene (H−C̈−NH2)—The
Simplest Amino Carbene." Angew. Chem. Int. Ed. 201857, 5248-5252, DOI: 10.1002/anie.201800679.


InChIs

1: InChI=1S/CH3N/c1-2/h1H,2H2
InChIKey=KASBEVXLSPWGFS-UHFFFAOYSA-N
2: InChI=1S/CH3N/c1-2/h2H,1H2
InChIKey=WDWDWGRYHDPSDS-UHFFFAOYSA-N

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Wednesday, July 18, 2018

Longest C–C Single Bond among Neutral Hydrocarbons with a Bond Length beyond 1.8 Å

Ishigaki, Y.; Shimajiri, T.; Takeda, T.; Katoono, R.; Suzuki, T., Chem 2018, 4, 795-806
Contributed by Steven Bacharach
Reposted from Computational Organic Chemistry with permission

This is a third post in a series dealing with very short or very long distances between atoms. Ishigaki, Shimajiri, Takeda, Katoono, and Suzuki have prepared three related analogues of hexaphenylethane that all have long C-C bonds.1 The idea is to create a core by fusing two adjacent phenyls into a naphylene, and then protect the long C-C bond through a shell made up of large aryl groups, 1. Fusing another 5-member ring opposite to the stretched C-C bond (2) creates a scissor effect that should stretch that bond further, even more so in the unsaturated version 3.

Their M062-x/6-31G* computations predict an increasing longer C-C bond (highlighted in blue in the above drawing): 1.730 Å in 1, 1.767 Å in 2, and 1.771 Å in 3. The structure of 3 is shown in Figure 3.

Figure 1. M06-2x/6-31G(d) optimized structure of 3.

These three compounds were synthesized, and characterized by IR and Raman spectroscopy. Their x-ray crystal structures at 200 K and 400K were also determined. The C-C distances are 1.742 Å (1), 1.773 Å (2) and 1.798 Å (3) with distances slightly longer at 400 K. These rank as the longest C-C bonds recorded.


References

1) Ishigaki, Y.; Shimajiri, T.; Takeda, T.; Katoono, R.; Suzuki, T., "Longest C–C Single Bond among Neutral Hydrocarbons with a Bond Length beyond 1.8 Å." Chem 20184, 795-806, DOI: 10.1016/j.chempr.2018.01.011.


InChIs

1: InChI=1S/C40H26/c1-5-17-32-27(11-1)23-24-28-12-2-6-18-33(28)39(32)36-21-9-15-31-16-10-22-37(38(31)36)40(39)34-19-7-3-13-29(34)25-26-30-14-4-8-20-35(30)40/h1-26H
InChIKey=IFIFQLGAOZULMX-UHFFFAOYSA-N
2: InChI=1S/C42H28/c1-5-13-33-27(9-1)17-18-28-10-2-6-14-34(28)41(33)37-25-23-31-21-22-32-24-26-38(40(37)39(31)32)42(41)35-15-7-3-11-29(35)19-20-30-12-4-8-16-36(30)42/h1-20,23-26H,21-22H2
InChIKey=HSJDZFLRPWJAGF-UHFFFAOYSA-N
3: InChI=1S/C42H26/c1-5-13-33-27(9-1)17-18-28-10-2-6-14-34(28)41(33)37-25-23-31-21-22-32-24-26-38(40(37)39(31)32)42(41)35-15-7-3-11-29(35)19-20-30-12-4-8-16-36(30)42/h1-26H
InChIKey=QQYNKBIOZSXWGD-UHFFFAOYSA-N

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