Installation instructions


ARC was only tested on Linux (Ubuntu 18.04.1 LTS) and Mac machines. We don’t expect it to work on Windows.

ARC can be installed on a server, as well as on your local desktop / laptop, submitting jobs to the server/s. The instructions below make this differentiation when relevant (the only difference is that ARC should be aware of software installed on the same machine, where the communication isn’t done via SSH).

Clone and setup path

  • Download and install the Anaconda Python Platform for Python 3.7 or higher if you haven’t already.

  • Get git if you don’t have it already by typing sudo apt-get install git in a terminal.

  • Clone ARC’s repository to by typing the following command in the desired folder (e.g., under ~/Code/):

    git clone
  • Add ARC to your local path in .bashrc (make sure to change ~/Path/to/ARC/ accordingly):

    export PYTHONPATH=$PYTHONPATH:~/Path/to/ARC/

Install dependencies

  • Install the latest DEVELOPER version of RMG (which has Arkane). It is recommended to follow RMG’s Developer installation by source using Anaconda instructions. Make sure to add RMG-Py to your PATH and PYTHONPATH variables as explained in RMG’s documentation.

  • If you’d like to use AutoTST in ARC (optional), clone it in a separate folder and add it to your PYTHONPATH just as well.

  • If you’d like to use a pre-trained graph convolutional network to quickly predict TS guesses for further optimization, run make gcn from ARC’s home directory, which clones TS-GCN under the same parent folder as the ARC repository. It also sets up the environment to run the network. The original network was published by Pattanaik et al. using TensorFlow 1.4 and Python 2.7. This repo uses the same architecture with various minor upgrades, more training data, and is translated into PyTorch Geometric and Python 3.7.

  • Create the Anaconda environment for ARC (after changing the directory to the installation folder by, e.g., cd ~/Code/ARC/):

    conda env create -f environment.yml

    Activate the ARC environment every time before you run ARC:

    conda activate arc_env

Create a .arc folder

Users are encouraged to create a .arc folder under their HOME folder on the machine running ARC. Copy (and modify as appropriate, see below) the following python files from the ARC repository into the newly created folder: <base_folder>/ARC/arc/settings/ –> HOME/.arc/ <base_folder>/ARC/arc/settings/ –> HOME/.arc/ <base_folder>/ARC/arc/settings/ –> HOME/.arc/

By doing this, ARC will use the respective settings and definitions from these copied files to override its defaults. Users many (carefully) modify the definitions in the local files as appropriate. Note that you may choose to copy only some of these files, in which case the definitions from any non-copied files will be taken from ARC’s defaults (e.g., most users will not need to modify Note also that definitions within these files may be partial (i.e., you may keep only those parameters you may wish to change within each file), and that any missing parameter will be assigned its default value from ARC’s defaults.

Principally ARC would also work fine if users directly change the respective files within ARC’s repository instead of making copies. However, modifying the files in ARC directly may cause merging conflicts when updating ARC. The down side is that users are responsible to keep their copies up to date with ARC’s format if major changes are made. Such changes will be listed under the Release Notes and will result in an increase of the MINOR version number (i.e., ,major.MINOR.patch, e.g., 1.1.5 –> 1.2.0).

Generating RSA SSH keys and defining servers

The first two directives are only required if you’d like ARC to access remote servers (ARC could also run “locally” on a server).

  • Generate RSA SSH keys for your favorite server/s on which relevant electronic structure software (ESS, e.g., Gaussian etc.) are installed. Instructions for generating RSA keys could be found here.

  • Copy the RSA SSH key path/s on your local machine to in the servers dictionary under keys.

  • Update the servers dictionary in your copy of ARC’s

    • A local server must be named with the reserved keyword local. cluster_soft and username (un) are mandatory.

    • A remote server has no limitations for naming. cluster_soft, address, username (un), and key (the path to the local RSA SSH key) are mandatory.

    • Optional parameters for both local and remote servers are cpus and memory. These two parameters stand for the maximum amount of cpu cores and memory in GB available to a node. If a job crashes due to cpu or memory issues, ARC will automatically re-run the job with different cpu and memory allocations within the limitation specified by these two parameters. By default, cpus is 8 and memory is 14 GB.

    • Although ARC currently does not allocate computing resources dynamically based on system size or ESS, the user can manually control memory specifications for each project. See Advanced Features for details.

    • In certain ESS, the maximum number of CPU cores allowed for a calculation depends on system size. If a job crashes for this reason, ARC will attempt to re-run the job with fewer CPU cores.

  • Update the default_job_settings dictionary in your copy of ARC’s

    • This dictionary contains default job memory, cpu, and time settings.

    • A default ESS job in ARC has 14 GB of memory, 8 cpu cores, and 120 hours of maximum execution time. The default settings can be changed by providing different values to the job_total_memory_gb, job_cpu_cores, and job_time_limit_hrs keys.

    • ARC will alter job memory, cpu, and time settings when troubleshoot jobs crashed due to resource allocation issues. The job_max_server_node_memory_allocation key stands for the maximum percentage of total node memory ARC will use when troubleshoot a job. The default value is 80%.

  • Update the submit scripts in your copy of ARC’s according to your servers’ definitions. * See the given template examples, and follow the structure of nested dictionaries (by server name, then by ESS name). * Preserve the variables in curly braces (e.g., {memory}), so that ARC is able to auto-complete them.

Associating software with servers

ARC keeps track of software location on servers using a Python dictionary associating the different software (keys) with the servers they are installed on (values). The server name must be consistent with the respective definition in the servers dictionary mentioned above. Typically, you would update the global_ess_settings dictionary in your copy of ARC’s to reflect your software and servers, for example:

global_ess_settings = {
    'gaussian': ['server1', 'server2'],
    'molpro': 'server2',
    'qchem': 'local',

Note that the above example reflects a situation where QChem in installed on the same machine as ARC, while Gaussian and Molpro are installed on different servers ARC has access to. You can of course make any combination as you’d like. The servers can be listed as a simple string for a single server, or as a list for multiple servers, where relevant.

These global settings are used by default unless ARC is given an ess_settings dictionary through an input file or the API, thus allowing more flexibility when running several instances of ARC simultaneously (e.g., if Gaussian is installed on two servers, where one has more memory in its nodes, the user can request ARC to use that specific server for the more memory-intensive jobs). More about the ess_settings dictionary can be found in the Advanced Features section of the documentation.

If neither global_ess_settings (in nor ess_settings (via an input file or the API) are specified, ARC will use its “radar” feature to “scans” the servers it has access to, and assign relevant ESS it is familiar with to the respective server. In order for this feature to function properly, make sure your .bashrc file on the remote servers does not have an interactive shell check. If it does, disable it.

It is recommended, though, to use the global_ess_settings and/or ess_settings dictionaries rather than allowing the “radar” to do its thing blindly. The “radar” feature, however, is very useful for diagnostics (see Tests below).

You can check what the “radar” detects using the ARC ESS diagnostics notebook.

Cluster software definitions

ARC supports Slurm and Oracle/Sun Grid Engine (OGE / SGE). If you’re using other cluster software, or if your server’s definitions are different that ARC’s, you should also modify the following variables in your copy of ARC’s

  • check_status_command

  • submit_command

  • delete_command

  • list_available_nodes_command

  • submit_filename

  • t_max_format

You will find the values for check_status_command, submit_command, delete_command, and list_available_nodes_command by typing on the respective server the which command, e.g.:

which sbatch

If you have different servers with the same cluster software that have different cluster software definitions, just name them differently, e.g., Slurm1 and Slurm2, and make sure to pair them accordingly under the servers dictionary.


  • If you’d like to make sure ARC has access to your servers and recognises your ESS, use the “radar” tool, available as an iPython notebook (see Standalone tools).

  • Run the minimal example (see Examples), and a couple more examples, if you’d like, using both input files and the API (via iPython notebooks or any other method).

  • Run ARC’s unit tests. Note that for all tests to pass, ARC expects to find the unmodified settings in Therefore it is recommended to first stash your changes. If you’d like to test ARC with changes you made to the code, first commit these changes to a git branch.:

    git stash
    make test

    After the tests complete, don’t forget to:

    git stash pop

Optional: Add ARC aliases to your .bashrc (for convenience)

Below are optional aliases to make ARC (even) more convenient (make sure to change ~/Path/to/ARC/ accordingly). Add these to your .bashrc file (edit it by typing, e.g., nano ~/.bashrc):

export arc_path=$HOME'/Path/to/ARC/'
alias arce='source activate arc_env'
alias arc='python $arc_path/ input.yml'
alias arcrestart='python $arc_path/ restart.yml'
alias arcode='cd $arc_path'
alias j='cd $arc_path/ipython/ && jupyter notebook'

Updating ARC

ARC is being updated frequently. Make sure to update ARC and enjoy new features and bug fixes.


If you change ARC’s parameters within the repository rather than copies thereof as explained above, it is highly recommended to backup the files you manually changed before updating ARC. These are usually ARC/arc/settings/ and ARC/arc/settings/

You can update ARC to a specific version, or to the most recent developer version. To get the most recent developer version, do the following (and make sure to change ~/Path/to/ARC/ accordingly):

cd ~/Path/to/ARC/
git stash
git fetch origin
git pull origin main
git stash pop

The above will update your main branch of ARC.

To update to a specific version (e.g., version 1.1.0), do the following (and make sure to change ~/Path/to/ARC/ accordingly):

cd ~/Path/to/ARC/
git stash
git fetch origin
git checkout tags/1.1.0 -b v1.1.0
git stash pop

The above will create a v1.1.0 branch which replicates the stable 1.1.0 version.

Note: This process might cause merge conflicts if the updated version (either the developer version or a stable version) changes a file you changed locally. Although we try to avoid causing merge conflicts for ARC’s users as much as we can, it could still sometimes happen. You’ll identify a merge conflict if git prints a message similar to this:

$ git merge BRANCH-NAME
> Auto-merging
> CONFLICT (content): Merge conflict in
> Automatic merge failed; fix conflicts and then commit the result

Detailed steps to resolve a git merge conflict can be found online.

Principally, you should open the files that have merge conflicts, and look for the following markings:

<<<<<<< HEAD
this is some content introduced by updating ARC
totally different content the user added, adding different changes
to the same lines that were also updated remotely
>>>>>>> new_branch_to_merge_later

Resolving a merge conflict consists of three stages:

  • determine which version of the code you’d like to keep (usually you should manually append your oun changes to the more updated ARC code). Make the changes and get rid of the unneeded <<<<<<< HEAD, =======, and >>>>>>> new_branch_to_merge_later markings. Repeat for all conflicts.

  • Stage the changed by typing: git add .

  • If you don’t plan to commit your changes, unstage them by typing: git reset --soft origin/main