Available Versions:
version 7.4.1 / version 7.3.1
Quantum ESPRESSO is an open-source suite widely used for electronic structure calculations and materials modeling at the nanoscale. It is based on density functional theory (DFT) and uses plane-wave basis sets to solve quantum mechanical equations for many-body systems.
With Inductiva, you can accelerate your Quantum ESPRESSO simulations by sending them to Cloud machines with hundreds of cores and terabytes of disk space.
Running your Quantum ESPRESSO simulations on the Cloud is easy. All you need is to create a short Python script that points Inductiva to the simulation artifacts you have on your computer, and we will take it from there.
On the right, we show how to use the Inductiva API to send a Quantum ESPRESSO simulation to a 180 vCPU machine (c3d-standard-180) hosted on Google Cloud (GCP).
You can copy paste this Python script, adapt it to your own case, and specify the Quantum ESPRESSO version you want to run. Your simulation will start right away, without waiting in a queue.
"""Quantum ESPRESSO example."""
import inductiva
# Allocate Google cloud machine
cloud_machine = inductiva.resources.MachineGroup( \
provider="GCP",
machine_type="c3d-highcpu-180")
# Initialize QuantumEspresso simulator
qe = inductiva.simulators.QuantumEspresso()
my_qe_command = [
# List the QE commands you wish to execute
]
# Run simulation
task = qe.run( \
input_dir="/path/to/my/quantumEspresso/files",
commands=my_qe_command,
on=cloud_machine)
# Wait for the simulation to finish and download the results
task.wait()
cloud_machine.terminate()
task.download_outputs()
We've got 22 simulators ready for you to explore.
Just one click away from running your favorite open-source simulators on the cloud and at scale!
Why not give it a try? Explore our example codes and discover everything our API can offer.
AMR-Wind
CaNS
COAWST
CP2K
DualSPHysics
FVCOM
FDS
GROMACS
GX
NWChem
OpenFAST
OpenFOAM (ESI)
OpenFOAM (Foundation)
OpenSees
Quantum ESPRESSO
REEF3D
SCHISM
SNL-SWAN
SPlisHSPlasH
SWAN
SWASH
XBeach