Wednesday, July 31, 2019

Popular Integration Grids Can Result in Large Errors in DFT-Computed Free Energies

Highlighted by Jan Jensen

 Figure 1B from the paper (CC BY-NC-ND 4.0)

 This paper has already been highlighted here and here, so I'll just briefly summarise.

The grid used for the numerical integration in DFT calculations is defined relative to the Cartesian axes, so rotating the molecule will change the integration grid and, hence, the energy. This has been known for some time and, f.eks. Gaussian09 uses a default grid size (Fine, 75,302) where the effect on the electronic energy variation is usually negligible.

Bootsma and Wheeler show that the vibrational entropy and, hence, the free energy is significantly more sensitive to grid size than the electronic energy. Using the Fine grid, the differences in relative free energy changes can be as large as 4 kcal/mol, which could significantly change conclusion regarding mechanisms, etc. The effect comes from the variation in low frequency vibrational modes and the effect can be reduced a little by scaling these frequencies. 

However, the errors really only become acceptable when using the UltraFine grid size, which is the default in Gaussian16, especially combined with frequency scaling (which one should do anyway to get consistent results). If you are using Gaussian09 or some other quantum program to compute relative free energies it is definitely a good idea to look at the default grid size and perform some tests.

Note that if you want to perform such tests yourself, you need to re-optimise the molecule after you rotate it because the gradient is also affected by the rotation.

Thursday, July 4, 2019

Combining the Power of J Coupling and DP4 Analysis on Stereochemical Assignments: The J-DP4 Methods

Grimblat, N.; Gavín, J. A.; Hernández Daranas, A.; Sarotti, A. M., Org. Letters 2019, 21, 4003-4007
Contributed by Steven Bachrach
Reposted from Computational Organic Chemistry with permission

I have written quite a number of posts on using quantum mechanics computations to predict NMR spectra that can aid in identifying chemical structure. Perhaps the most robust technique is Goodman’s DP4 method (post), which has seen some recent revisions (updated DP4DP4+). I have also posted on the use of computed coupling constants (posts).

Grimblat, Gavín, Daranas and Sarotti have now combined these two approaches, using computed 1H and 13C chemical shifts and 3JHH coupling constants with the DP4 framework to predict chemical structure.1

They describe two different approaches to incorporate coupling constants:
  • dJ-DP4 (direct method) incorporates the coupling constants into a new probability function, using the coupling constants in an analogous way as chemical shifts. This requires explicit computation of all chemical shifts and 3JHH coupling constants for all low-energy conformations.
  • iJ-DP4 (indirect method) uses the experimental coupling constants to set conformational constraints thereby reducing the number of total conformations that need be sampled. Thus, large values of the coupling constant (3JHH > 8 Hz) selects conformations with coplanar hydrogens, while small values (3JHH < 4 Hz) selects conformations with perpendicular hydrogens. Other values are ignored. Typically, only one or two coupling constants are used to select the viable conformations.

The authors test these two variants on 69 molecules. The original DP4 method predicted the correct stereoisomer for 75% of the examples, while dJ-DP4 correct identifies 96% of the cases. As a test of the indirect method, they examined marilzabicycloallenes A and B (1 and 2). DP4 predicts the correct stereoisomer with only 3.1% (1) or <0.1% (2) probability. dJ-DP4 predicts the correct isomer for 1 with 99.9% probability and 97.6% probability for 2. The advantage of iJ-DP4 is that using one coupling constant reduces the number of conformations that must be computed by 84%, yet maintains a probability of getting the correct assignment at 99.2% or better. Using two coupling constants to constrain conformations means that only 7% of all of the conformations need to be samples, and the predictive power is maintained.


Both of these new methods clearly deserve further application.


1. Grimblat, N.; Gavín, J. A.; Hernández Daranas, A.; Sarotti, A. M., “Combining the Power of J Coupling and DP4 Analysis on Stereochemical Assignments: The J-DP4 Methods.” Org. Letters 201921, 4003-4007, DOI: 10.1021/acs.orglett.9b01193.


1: InChI=1S/C15H21Br2ClO4/c1-8-15(20)14-6-10(17)12(19)7-11(18)13(22-14)5-9(21-8)3-2-4-16/h3-4,8-15,19-20H,5-7H2,1H3/t2-,8-,9+,10-,11+,12+,13+,14+,15-/m0/s1
2: InChI=1S/C15H21Br2ClO4/c1-8-15(20)14-6-10(17)12(19)7-11(18)13(22-14)5-9(21-8)3-2-4-16/h3-4,8-15,19-20H,5-7H2,1H3/t2-,8-,9-,10-,11+,12+,13+,14+,15-/m0/s1

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