Software Open Access

GoodVibes: version 2.0.3

Ignacio Funes-Ardoiz; Robert S. Paton

Researcher(s)
Jaime Rodríguez-Guerra; JingTao Chen

A Python program to compute quasi-harmonic thermochemical data from Gaussian frequency calculations at a given temperature/concentration, corrected for the effects of vibrational scaling-factors and available free space in solvent. Developed by Robert Paton (Colorado State & Oxford) and Ignacio Funes-Ardoiz (ICIQ). Integration with Travis CI testing by Jaime Rodríguez-Guerra.

All (electronic, translational, rotational and vibrational) partition functions are recomputed and will be adjusted to any temperature or concentration. These default to 298.15 K and 1 atmosphere.

The quasi-harmonic approximation is applied to the vibrational entropy: below a given cut-off value vibrational normal modes are not well described by the rigid-rotor-harmonic-oscillator (RRHO) approximation and an alternative expression is instead used to compute the associated entropy. The quasi-harmonic vibrational entropy is always less than or equal to the standard (RRHO) value obtained using Gaussian. Two literature approaches have been implemented. In the simplest approach, from Cramer and Truhlar,1 all frequencies below the cut-off are uniformly shifted up to the cut-off value before entropy calculation in the RRHO approximation. Alternatively, as proposed by Grimme,2 entropic terms for frequencies below the cut-off are obtained from the free-rotor approximation; for those above the RRHO expression is retained. A damping function is used to interpolate between these two expressions close to the cut-off frequency.

The program will attempt to parse the level of theory and basis set used in the calculations and then try to apply the appropriate vibrational (zpe) scaling factor. Scaling factors are taken from the Truhlar group database.

 

Installation

  • With pypi: pip install goodvibes
  • With conda: conda install -c patonlab goodvibes
  • Manually Cloning the repository https://github.com/bobbypaton/GoodVibes.git and then adding the location of the GoodVibes directory to the PYTHONPATH environment variable.
  • Run the script with your Gaussian output files (the program expects log or out extensions). It has been tested with Python 2 and 3 on Linux, OSX and Windows

 

Correct Usage

python -m goodvibes [-q grimme/truhlar] [-f cutoff_freq] [-t temperature] [-c concentration] [-v scalefactor] [-s solvent name] [--spc link/filename] [--xyz] [--imag] [--cpu] [--ti 't_initial, t_final, step'] [--ci 'c_initial, c_final, step'] <gaussian_output_file(s)>
  • The -h option gives help by listing all available options, default values and units, and proper usage.
  • The -q option selects the approximation for the quasiharmonic entropic correction: -q truhlar or -q grimmerequest the options explained above. Both avoid the tendency of RRHO vibrational entropies towards infinite values for low frequecies. If not specified this defaults to Grimme's expression.
  • The -f option specifies the frequency cut-off (in wavenumbers) i.e. -f 50 would use 50 cm-1. The default value is 100 cm-1. N.B. when set to zero all thermochemical values match standard (i.e. harmonic) Gaussian quantities.
  • The -t option specifies temperature (in Kelvin). N.B. This does not have to correspond to the temperature used in the Gaussian calculation since all thermal quantities are reevalulated by GoodVibes at the requested temperature. The default value is 298.15 K.
  • The -c option specifies concentration (in mol/l). It is important to notice that the ideal gas approximation is used to relate the concentration with the pressure, so this option is the same as the Gaussian Pressure route line specification. The correction is applied to the Sackur-Tetrode equation of the translational entropy e.g. -c 1 corrects to a solution-phase standard state of 1 mol/l. The default is 1 atmosphere.
  • The -v option is a scaling factor for vibrational frequencies. DFT-computed harmonic frequencies tend to overestimate experimentally measured IR and Raman absorptions. Empirical scaling factors have been determined for several functional/basis set combinations, and these are applied automatically using values from the Truhlar group3based on detection of the level of theory and basis set in the output files. This correction scales the ZPE by the same factor, and also affects vibrational entropies. The default value when no scaling factor is available is 1 (no scale factor). The automated scaling can also be surpressed by -v 1.0
  • The --ti option specifies a temperature interval (for example to see how a free energy barrier changes with the temperature). Usage is --ti 'initial_temperature, final_temperature, step_size'. The step_size is optional, the default is set by the relationship (final_temp-initial_temp) /10
  • The -s option specifies the solvent. The amount of free space accessible to the solute is computed based on the solvent's molecular and bulk densities. This is then used to correct the volume available to each molecule from the ideal gas approximation used in the Sackur-Tetrode calculation of translational entropy, as proposed by Shakhnovich and Whitesides.4 The keywords H2O, toluene, DMF (N,N-dimethylformamide), AcOH (acetic acid) and chloroform are recognized.
  • the --spc option can be used to obtain single point energy corrected values. For multi-step jobs in which a frequency calculation is followed by an additional (e.g. single point energy) calculation, the energy is taken from the final job and all thermal corrections are taken from the frequency calculation. Alternatively, the energy can be taken from an additional file.
  • The --xyz option will write all Cartesian coordinates to an xyz file.
  • the --imag option will print any imaginary frequencies (in wavenumbers) for each structure. Presently, all are reported. The hard-coded variable im_freq_cutoff can be edited to change this. To generate new input files (i.e. if this is an undesirable imaginary frequency) see pyQRC
  • the --cpu option will add up all of the CPU time across all files (including single point calculations if requested).

 

Example usage is shown in the GitHub README

 

Tips and Troubleshooting

  • The python file doesn’t need to be in the same folder as the Gaussian files. Just set the location of GoodVibes.py in the $PATH variable
  • It is possible to run on any number of files at once, for example using wildcards to specify all of the Gaussian files in a directory (*.out)
  • File names not in the form of filename.log or filename.out are not read
  • The script will not work if terse output was requested in the Gaussian job

 

Papers citing GoodVibes

  1. Li, Y.; Du, S. RSC Adv. 20166, 84177-84186 DOI: 10.1039/C6RA16321A
  2. Myllys, N.; Elm, J.; Kurtén, T. Comp. Theor. Chem. 20161098, 1–12 DOI: 10.1016/j.comptc.2016.10.015
  3. Kiss, E.; Campbell, C. D.; Driver, R. W.; Jolliffe, J. D.; Lang, R.; Sergeieva, T.; Okovytyy, S.; Paton, R. S.; Smith, M. D. Angew. Chem. Int. Ed. 2016128 14017-14021 DOI: 10.1002/ange.201608534
  4. Mohamed, S.; Krenske, E. H.; Ferro, V. Org. Biomol. Chem. 201614, 2950-2960 DOI: 10.1039/c6ob00283h
  5. Deb, A.; Hazra, A.; Peng, Q.; Paton, R. S.; Maiti, D. J. Am. Chem. Soc. 2017139, 763–775 DOI: 10.1021/jacs.6b10309
  6. Simón, L.; Paton, R. S. J. Org. Chem. 201782, 3855-3863 DOI: 10.1021/acs.joc.7b00540
  7. Grayson, M. N. J. Org. Chem. 201782, 4396–4401 DOI: 10.1021/acs.joc.7b00521
  8. Duarte, F.; Paton, R. S. J. Am. Chem. Soc. 2017139, 8886-8896 DOI: 10.1021/jacs.7b02468
  9. Elm, J. J. Phys. Chem. A 2017121, 8288−8295 DOI: 10.1021/acs.jpca.7b08962
  10. Münster, N.; Parker, N. A.; van Dijk, L.; Paton, R. S.; Smith, M. D. Angew. Chem. Int. Ed. 201756, 9468-9472 DOI:10.1002/anie.201705333
  11. Mekareeya, A.; Walker, P. R.; Couce-Rios, A.; Campbell, C. D.; Steven, A.; Paton, R. S.; Anderson, E. A. J. Am. Chem. Soc. 2017139, 10104–10114 DOI 10.1021/jacs.7b05436
  12. Alegre-Requena, J. V.; Marqués-López, E.; Herrera, R. P. ACS Catal. 20177, 6430–6439 DOI:10.1021/acscatal.7b02446
  13. Elm, J. J. Phys. Chem. A 2017121, 8288–8295 DOI: 10.1021/acs.jpca.7b08962
  14. Li, Y.; Jackson, K. E.; Charlton, A.; Le Neve-Foster, B.; Khurshid, A.; Rudy, H.-K. A.; Thompson, A. L.; Paton, R. S.; Hodgson, D. M. J. Org. Chem. 201782, 10479-10488 DOI: 10.1021/acs.joc.7b01954
  15. Alegre-Requena, J. V.; Marqués-López, E.; Herrera, R. P. Chem. Eur. J. 201723, 15336–15347DOI:10.1002/chem.201702841
  16. Funes‐Ardoiz, I.; Nelson, D. J.; Maseras, F. Chem. Eur. J. 201723, 16728–16733 DOI: 10.1002/chem.201702331
  17. Morris, D. S.; van Rees, K.; Curcio, M.; Cokoja, M.; Kühn, F. E.; Duarte, F.; Love, J. B. Catal. Sci. Technol. 2017, 5644–5649 DOI: 10.1039/C7CY01728F
  18. Besora, M.; Vidossich, P.; Lledos, A.; Ujaque, G.; Maseras, F. J. Phys. Chem. A 2018122, 1392–1399 DOI:10.1021/acs.jpca.7b11580
  19. Harada, T. J. Org. Chem. 201883, 7825–7835 DOI: 10.1021/acs.joc.8b00712
  20. Lewis, R. D.; Garcia-Borràs, M.; Chalkley, M. J.; Buller, A. R.; Houk, K. N.; Kan, S. B. J.; Arnold, F. H. Proc. Natl. Acad. Sci. 2018115, 7308-7313 DOI: 10.1073/pnas.1807027115

 

References to the underlying theory

  1. Ribeiro, R. F.; Marenich, A. V.; Cramer, C. J.; Truhlar, D. G. J. Phys. Chem. B 2011115, 14556-14562 DOI:10.1021/jp205508z
  2. Grimme, S. Chem. Eur. J. 201218, 9955–9964 DOI: 10.1002/chem.201200497
  3. Alecu, I. M.; Zheng, J.; Zhao, Y.; Truhlar, D. G.; J. Chem. Theory Comput. 20106, 2872-2887 DOI: 10.1021/ct100326h
  4. Mammen, M.; Shakhnovich, E. I.; Deutch, J. M.; Whitesides, G. M. J. Org. Chem. 199863, 3821-3830 DOI:10.1021/jo970944f

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