Quantum ESPRESSO Available Versions:
version 7.3.1
Available Versions:
version 7.3.1
Quantum ESPRESSO is an open-source software 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. The package is highly extensible, enabling simulations of a variety of material properties, including electronic, vibrational, and magnetic characteristics. Researchers value it for its flexibility, scalability on high-performance computing platforms, and its role in advancing quantum simulations and computational materials science.
At Inductiva, we have compiled two versions of Quantum ESPRESSO: one for MPI and the other for OpenMP. To run the MPI version, simply use the standard command names (e.g., pw.x). For the OpenMP version, append _openmp to the command names (e.g., pw_openmp.x). This allows users to choose the most suitable version based on their needs.
To know the list of available commands, visit our Quantum ESPRESSO tutorials to learn more.
Give it a Go: Try Quantum ESPRESSO on the Cloud with Inductiva!
This code example shows how to run a Quantum ESPRESSO simulation on a c3d-standard-180 Google Cloud machine using our API. To try it out, simply paste the code into your Python environment.
For more details on the simulator’s features and configurations, visit the official Quantum ESPRESSO site.
"""Quantum ESPRESSO example."""
import inductiva
# Allocate Google cloud machine
cloud_machine = inductiva.resources.MachineGroup( \
provider="GCP",
machine_type="c3d-standard-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()
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