v23.04.03#
A Virtual Machine for Computational Materials Science
Installation instructions#
Get Quantum Mobile running on your computer in three simple steps:
Download virtual machine image (5.9 GB)
URL: https://drive.google.com/file/d/1dhBANfO7KdaFWu_rwkBduKYNjofu8k33/view?usp=sharing
Filename:
quantum_mobile_23.04.03.ova
MD5 hash:
3d489840c77b27afe1d2367abb5ac44d
Install Virtual Box 6.1.6 or later (see https://www.virtualbox.org)
Import virtual machine image into Virtualbox (15.7 GB) File => Import Appliance
Login credentials: username: max
, password: moritz
.
The default configuration of 2
cores and 1536
MB RAM can be adjusted in the VM settings.
Alternatively, this Quantum Mobile is available as a Docker image: https://hub.docker.com/r/marvelnccr/quantum-mobile
Contact#
For issues encountered during installation, please first consult the FAQ page.
Please direct inquiries regarding Quantum Mobile to the Quantum Mobile support channel in the AiiDA Discourse
Changelog#
Update build tool to VirtualBox version 7 (https://www.virtualbox.org/wiki/Changelog-7.0)
Install quantum-espresso via Conda (rather than direct compilation)
Add more AiiDA plugins/codes, compatible with aiida-core v2 (see marvel-nccr/quantum-mobile#211)
Add
verdi code test
run, after code creation, to test it is working correctlyAdd
aiida-examples
scripts folder, these run a basic calculation example (with MPI), and test it completes correctly
Build Process#
OS:
MacOSX
Ansible:
2.10.17
Vagrant:
2.3.4
Virtualbox:
7.0.6r155176
Base VM Image:
bento/ubuntu-20.04
Software Summary#
[Quantum Mobile]
version = 23.04.03
Operating System = Ubuntu 20.04.6 LTS
Login credentials = max / moritz
[Apt packages]
grace = 1:5.1.25-7build1
xcrysden = 1.6.2-3build1
default-jre = 2:1.11-72
rabbitmq-server = 3.8.2-0ubuntu1.4
postgresql-client = 12+214ubuntu0.1
[Conda 'aiida' environment]
aiida-abinit = 0.4.0-pyhd8ed1ab_0@conda-forge
aiida-core = 2.2.2-pyh1a96a4e_1@conda-forge
aiida-core.notebook = 2.2.2-pyh1a96a4e_1@conda-forge
aiida-cp2k = 2.0.0-pyhd8ed1ab_1@conda-forge
aiida-nwchem = 2.1.0-pyhd8ed1ab_0@conda-forge
aiida-pseudo = 1.0.0-pyhd8ed1ab_0@conda-forge
aiida-quantumespresso = 4.2.0-pyhd8ed1ab_0@conda-forge
aiida-siesta = 2.0.0-pyhd8ed1ab_0@conda-forge
ipykernel = 6.22.0-pyh210e3f2_0@conda-forge
jupyterlab = 3.5.3-pyhd8ed1ab_0@conda-forge
jupyterlab-spellchecker = 0.7.3-pyhd8ed1ab_0@conda-forge
jupyterlab-tour = 3.1.4-pyhd8ed1ab_0@conda-forge
mamba_gator = 5.2.0-pyhd8ed1ab_0@conda-forge
pip = 23.0.1-pyhd8ed1ab_0@conda-forge
python = 3.9.16-h2782a2a_0_cpython@conda-forge
[Conda 'abinit' environment]
abinit = 9.8.3-hd1b6b71_2@conda-forge
libxc = 4.3.4-h86c2bf4_2@conda-forge
mpich = 4.0.3-h846660c_100@conda-forge
[Conda 'bigdft' environment]
bigdft-suite = 1.9.3-mpi_mpich_py311h71b1498_0@conda-forge
libxc = 4.3.4-h86c2bf4_2@conda-forge
mpich = 4.0.3-h846660c_100@conda-forge
[Conda 'cp2k' environment]
cp2k = 9.1.0-py39_openmpi_0@conda-forge
libxc = 5.2.3-py311h9e0c992_2@conda-forge
openmpi = 4.1.2-hbfc84c5_0@conda-forge
[Conda 'fleur' environment]
fleur = 6.1-h005d346_1@conda-forge
libxc = 5.2.3-py311h9e0c992_2@conda-forge
openmpi = 4.1.2-hbfc84c5_0@conda-forge
[Conda 'nwchem' environment]
libxc = 5.2.3-py39hea1df8f_2@conda-forge
nwchem = 7.0.2-py39hea0d9f8_3@conda-forge
openmpi = 4.1.2-hbfc84c5_0@conda-forge
[Conda 'qespresso' environment]
libxc = 5.2.3-py311h9e0c992_2@conda-forge
openmpi = 4.1.2-hbfc84c5_0@conda-forge
qe = 7.0-he8a42d8_1@conda-forge
[Conda 'siesta' environment]
libxc = 5.2.3-py311h9e0c992_2@conda-forge
openmpi = 4.1.2-hbfc84c5_0@conda-forge
siesta = 4.1.5-mpi_openmpi_hfab99a0_2@conda-forge
[Conda 'yambo' environment]
libxc = 5.2.3-py311h9e0c992_2@conda-forge
openmpi = 4.1.2-hbfc84c5_0@conda-forge
yambo = 5.0.4-h6b7a505_1@conda-forge
[Conda 'wannier90' environment]
libxc = 5.2.3-py311h9e0c992_2@conda-forge
openmpi = 4.1.2-hbfc84c5_0@conda-forge
wannier90 = 3.1.0-hb97063f_2@conda-forge
[Conda 'visualise' environment]
cif2cell = 2.0.0a3-pyhd8ed1ab_0@conda-forge
gnuplot = 5.4.5-h142138f_1@conda-forge
jmol = 14.32.10-ha770c72_0@conda-forge
python = 3.9.16-h2782a2a_0_cpython@conda-forge
[Pseudopotentials]
SSSP/PBE/efficiency/1.1 = /usr/local/share/pseudo_sssp_PBE_efficiency_1.1
DOJO/PBE/FR/standard/0.4/psml = /usr/local/share/pseudo_dojo_PBE_FR_standard_0.4_psml