Author Archives: Alexandra Cassell

I have been having to process and handle a wide variety of errors in the past few weeks, using Gaussian as well as Comet. Here are the conclusions we found if you ever run into similar errors.

1. FileIO error:   This error was quite odd and took a lot of work to surpass. It was seen in at least three excited state optimization calculation output files but there was no other trend visible based on functional, basis set, or root number. This image is difficult to read but it essentially is a very long list of groups of numbers (like at the top), followed by “dumping /fiocom/ . . .  . . Ntrerr called from FileIO”. After communicating with Gaussian Tech Support, they offered the advice of updating our Gaussian to the E version. To do this, go to the gaussian.sh file and where it says “module load gaussian,” correct it to “module load gaussian.09.E.01” and save. This error was due to a bug in the older version that we had been using, although it could have been circumvented using “opt=nolinear” or “guess=always” as a keyword. For this reason, we have been using version E of Gaussian for all further calculations. 
2. Python plots: In the energy_step_oscillator_plot.py script, I had difficult writing to the results file. Whenever I would run the module, the correct plot would pop up but then the results file would be empty (blank and 0KB). To see the proper results, make sure to refresh the folder in which the results.txt file is and also close the plot before opening the results. This is necessary if and only if your script reads to “results.close()” AFTER the plot order. If you simply move “results.close()” to before the plot instructions, this will not be an issue. Now, the results.txt file will close before the plots are displayed.
3. There have been multiple different cases where jobs on comet required more memory. There would be a message towards the end of the prematurely terminated output file asking for more MW, which is a different unit of memory storage. I have been adding “%Mem=8GB” to almost all of my input .gjf files (as the first line) to ensure that the job doesn’t end early.
4. If a job terminates otherwise but there is no clear explanation, this probably indicates that there was not enough wall time to perform the calculation entirely, so you should add more wall time or increase the number of processors you use for the calculation. To do so, you must change “nprocshared=1” to “nprocshared=8” and “ntasks-per-node=1” to “ntasks-per-node=8” or any number between 1 and 8, and also include “%nprocshared=#” at the top of your input file. This can show a dramatic decrease in the time necessary to perform certain calculations.

There are multiple types of functionals and basis sets that can be used for different calculations in Gaussian such as optimizations, scans, and excited state energy calculations. A basis set is a set of basis functions. Each basis set is a different size and generally, the bigger the basis set size, the more accurate the results will be. The names of the basis sets accessible through Gaussian are 6-31G (which can include +,++, and different orbitals), STO-3G, 3-21G, 6-311G, cc-pVDZ, cc-pVTZ, cc-pVQZ, LanL2DZ, LanL2MB, SDD, DGDZVP, DGDZVP2, DGTZVP, GEN, and GENECP. However, really there are many more options available which are discussed more thoroughly on the following website (http://www.gaussian.com/g_tech/g_ur/m_basis_sets.htm). It is also possible to create your own basis set using Gaussian, but this can be time-consuming and complicated. In relation to 6-31G, the increasing size of the basis set in terms of +, ++, aug- (which are augmented basis sets)  and p,d,f orbitals or *,** (polarization functions), the more that are included, the more accurate results these should be as well. Each basis set contains a different number of Cartesian (etc) basis functions, which can be found in the output file (ctrl-f “basis function”). The larger the number of basis functions corresponds to a longer calculation time.

I performed an optimization calculation on a new conformation of tryptophan and then ran excited state calculations using 16 combinations of functionals (b3lyp, cam-b3lyp, pbepbe, and wb97xd) and basis sets (6-31G, 6-31+G, 6-31+G(d,p), and cc-pVDZ). Since 6-31G is the smallest basis set here, it took the shortest time to complete calculations in all of the functionals. Also, within functionals, cc-pVDZ is similar in time to 6-31G. Below is a table showing the times and number of basis functions for each basis set that was used in calculating excited state energies of an optimized configuration of tryptophan.

A) Basis set: 6-31G

Cartesian Basis functions: 159

B) Basis set: 6-31+G

Cartesian Basis functions: 219

C) Basis set: 6-31+G(d,p)

Cartesian Basis functions: 345

D) Basis set: cc-pvdz

Cartesian basis functions: 285