Gaussian 16 Linux [ 2027 ]

export g16root=/opt export GAUSS_SCRDIR=/local/scratch/$PBS_JOBID export GAUSS_PDEF=64 export GAUSS_MDEF=128GB

g16 < $SLURM_SUBMIT_DIR/input.com > $SLURM_SUBMIT_DIR/output.log

The Gaussian website (gaussian.com) has extensive documentation, including the Running Gaussian guide and the Link 0 Commands page.

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Optionally, you may also set:

Whether running on a single workstation with multiple cores or a large-scale cluster (MPI), G16 scales effectively.

Installing Gaussian 16 on Linux is a straightforward process when following the official binary installation instructions. If you share with third parties, their policies apply

Summary

Gaussian 16 uses input files conventionally named with .gjf or .com extensions. A minimal input file follows this structure:

Before running Gaussian 16 on Linux, make sure your system meets the minimum requirements: Optionally, you may also set: Whether running on

, providing an intuitive graphical interface for building complex structures and visualizing results on Linux. Flexible Environment:

sudo mkdir /scratch_gauss sudo mount -t tmpfs -o size=32G tmpfs /scratch_gauss export GAUSS_SCRDIR=/scratch_gauss

If you have a precompiled tarball (e.g., g16.tbz ), copy it to the target directory and extract it. You must use the appropriate tar command, typically tar xvf with the relevant flags.

| Component | Recommended Specification | Notes | | :--- | :--- | :--- | | | ~2-3 GB (executables) + 200 GB+ (scratch) | The program files require 2-3 GB. For large jobs, especially post-SCF calculations like MP2 or CCSD, temporary scratch space of up to 200 GB or more is essential. | | Memory (RAM) | 256 GB or higher | While the default memory allocation in Gaussian is 800 MB, computational nodes for production runs often feature 256 GB or more to handle large systems. | | Swap Space | 1-2 GB | This is in addition to physical RAM. | | CPU | Modern x86_64 with AVX2 support | For optimal performance, CPUs that support the AVX2 instruction set (e.g., Intel Haswell or newer) are highly recommended. |

Gaussian 16 on Linux is suitable for: