Gaussian 16 is a connected series of programs for performing semi-empirical, density functional theory and ab initio molecular orbital calculations. Starting from the fundamental laws of quantum mechanics, Gaussian 16 predicts the energies, molecular structures, vibrational frequencies and molecular properties of molecules and reactions in a wide variety of chemical environments. Gaussian 16’s models can be applied to both stable species and compounds which are difficult or impossible to observe experimentally (e.g., short-lived intermediates and transition structures).

Basic Usage

Load the Gaussian Module as follows:

module load gaussian

For an example job script, see below:

# Example for 12 processors
#$ -N gaussian_job_name
#$ -pe smp 12

module load gaussian



The Gaussian file MUST include the right syntax and number of processes.

For the example above the file must contain the following instruction:


Useful Options

Scratch Space

If you expect your Gaussian jobs to write large amounts of data to scratch files at runtime then you should change the SCRATCH directory to point to storage space on your /scratch365 allocation. You can do this in your submission scripts by setting the following environment variable:

setenv GAUSS_SCRDIR /scratch365/netid/directory

Memory and Parallelism

Some jobs, especially MP2, may consume large memory and disk storage resources. Instead of running these kinds of jobs in distributed memory Linda-parallel mode it might be better to use a shared-memory parallel approach. For larger systems Gaussian16 also allows a mixed-mode approach using shared-memory-parallelism within nodes and Linda only between nodes.

Using shared memory parallel execution can save a lot of disk space usage (roughly eight times), since tasks on the same node will share a copy of the scratch file whereas each Linda-parallel task will create its own copy of the scratch data file. The savings of up to a factor of eight can be quite significant because the minimum disk required for MP2 frequencies is a multiple of N^4 (where N is the number of basis functions).

For a one-node job (eight cores) use, for example, something like:


For very large jobs, you might consider setting two Gaussian16 parameters, %Mem and %MaxDisk, that affect the amount of memory and disk, respectively, in order to produce good general performance. For the types of calculations that obey %MaxDisk, the disk usage will be kept below this value. See the Gaussian Efficiency Considerations web page for details. There are some examples in the directory $g16root/g16/tests/com.

When using multiple processors with shared memory, a good estimate of the memory required is the amount of memory required for a single processor job times the number of cores used per node. In other words, %Mem represents the total memory requested for each node. For distributed memory calculations using Linda, the amount of memory specified in %Mem should be equal to or greater than the value for a single processor job.

Further Information

See the official website: Gaussian