Run Your First Simulation

A typical GROMACS simulation involves several key steps: preparing the molecular structure, performing energy minimization, running the simulation, and analyzing the results. You’ll need multiple input files to configure and run your simulation, and specific GROMACS commands will be used to carry out each step.

This tutorial will show you how to run GROMACS simulations using the Inductiva API.

We will cover the Bulk salt solution use case from the GROMACS tutorials page to help you get started with simulations.

Prerequisites

To generate the input files required for this simulation, please complete the initial steps of this section of the tutorial, as well as the Energy Minimization part.

After completing these steps, you should have the following files in your SimulationFiles folder:

├── ./conf.gro
├── ./ff
│   ├── ./ff/forcefield.itp
│   ├── ./ff/h2o.itp
│   ├── ./ff/na.itp
│   └── ./ff/so4.itp
├── ./min.mdp
└── ./topol.top

With these files in place, you're ready to send your simulation to the cloud!

Running an GROMACS Simulation

Here is the code required to run a GROMACS simulation using the Inductiva API:

"""GROMACS example."""
import inductiva

# Allocate cloud machine on Google Cloud Platform
cloud_machine = inductiva.resources.MachineGroup( \
    provider="GCP",
    machine_type="c2d-highcpu-16",
    spot=True)

# List of commands to run
commands = [
    "gmx grompp -f min.mdp -c conf.gro -p topol.top -o min -pp min -po min",
    "gmx mdrun -v -deffnm min",
]

# Initialize the Simulator
gromacs = inductiva.simulators.GROMACS( \
    version="2025.0")

# Run simulation
task = gromacs.run( \
    input_dir="/Path/to/SimulationFiles",
    commands=commands,
    on=cloud_machine)

# Wait for the simulation to finish and download the results
task.wait()
cloud_machine.terminate()

task.download_outputs()

task.print_summary()

In this basic example, we're using a cloud machine (c2d-highcpu-16) equipped with 16 virtual CPUs. For larger or more compute-intensive simulations, consider adjusting the machine_type parameter to select a machine with more virtual CPUs or one equipped with GPUs. You can explore the full range of available machines here.

Note: Setting spot=True enables the use of spot machines, which are available at substantial discounts. However, your simulation may be interrupted if the cloud provider reclaims the machine.

To adapt this script for other GROMACS simulations, replace input_dir with the path to your GROMACS input files and set the commands accordingly. Be sure to specify the GROMACS version compatible with your input files.

When the simulation is complete, we terminate the machine, download the results and print a summary of the simulation as shown below.

Task status: Success

Timeline:
    Waiting for Input         at 22/04, 09:28:14      0.875 s
    In Queue                  at 22/04, 09:28:15      34.39 s
    Preparing to Compute      at 22/04, 09:28:49      1.699 s
    In Progress               at 22/04, 09:28:51      3.056 s
        ├> 1.915 s         gmx grompp -f min.mdp -c conf.gro -p topol.top -o min -pp min -po min
        └> 0.983 s         gmx mdrun -v -deffnm min
    Finalizing                at 22/04, 09:28:54      0.485 s
    Success                   at 22/04, 09:28:54

Data:
    Size of zipped output:    1.75 MB
    Size of unzipped output:  2.29 MB
    Number of output files:   9

Total estimated cost (US$): 0.01020 US$
    Estimated computation cost (US$): 0.00020 US$
    Task orchestration fee (US$): 0.010 US$

Note: A per-run orchestration fee (0.010 US$) applies to tasks run from 01 Dec 2025, in addition to the computation costs.
Learn more about costs at: https://inductiva.ai/guides/how-it-works/basics/how-much-does-it-cost

As you can see in the "In Progress" line, the part of the timeline that represents the actual execution of the simulation, the core computation time of this simulation was approximately 3.1 seconds.