Institute for Computational Physics

Featured Dataverses

In order to use this feature you must have at least one published or linked dataverse.

Publish Dataverse

Are you sure you want to publish your dataverse? Once you do so it must remain published.

Publish Dataverse

This dataverse cannot be published because the dataverse it is in has not been published.

Delete Dataverse

Are you sure you want to delete your dataverse? You cannot undelete this dataverse.

11 to 20 of 692 Results

espresso.patch Apr 1, 2026 - Replication Data for: Coupling ESPResSo and waLBerla for scalable soft matter simulations with hydrodynamic interactions Unknown - 2.7 KB - MD5: 2cac212fec13dc1c7913276cd1158e98 Patch to improve ESPResSo's GPU algorithms performance on the MareNostrum 5 supercomputer.
espresso_build.sh Apr 1, 2026 - Replication Data for: Coupling ESPResSo and waLBerla for scalable soft matter simulations with hydrodynamic interactions Unknown - 3.0 KB - MD5: 143e920d34e136c527cfdee639ac047f Build commands for ESPResSo and waLBerla using the Nvidia HPC SDK 25.7 toolchain and the EESSI foss/2025a toolchain. Network connections to the MareNostrum supercomputer must be done through SSH, therefore the building of software requires extra steps.
espresso_fetch.sh Apr 1, 2026 - Replication Data for: Coupling ESPResSo and waLBerla for scalable soft matter simulations with hydrodynamic interactions Unknown - 793 B - MD5: fdb2f1196bb37f138f13b6caf422c109 Download commands for the ESPResSo software and third-party libraries included via the CMake FetchContent mechanism. Network connections to the MareNostrum supercomputer must be done through SSH, therefore software must be downloaded locally and uploaded to the supercomputer via SSH.
python_build.sh Apr 1, 2026 - Replication Data for: Coupling ESPResSo and waLBerla for scalable soft matter simulations with hydrodynamic interactions Unknown - 448 B - MD5: 487249e24125b0f9e07239f293769857 Build commands for Python, and virtual environment setup.
python_fetch.sh Apr 1, 2026 - Replication Data for: Coupling ESPResSo and waLBerla for scalable soft matter simulations with hydrodynamic interactions Unknown - 447 B - MD5: 24b6109346b0edf465c1627263ff1061 Download commands for the Python source code and Python package wheels. Network connections to the MareNostrum supercomputer must be done through SSH, therefore software must be downloaded locally and uploaded to the supercomputer via SSH.
lb_gpu_coupling.tab Apr 1, 2026 - Replication Data for: Coupling ESPResSo and waLBerla for scalable soft matter simulations with hydrodynamic interactions Tabular Data - 10.3 KB - 22 Variables, 79 Observations - UNF:6:nyTW13JPg/FmjWXLpzuHhQ== Benchmark results for LB simulations with particle coupling (job_coupling.py).
lb_gpu_io.tab Apr 1, 2026 - Replication Data for: Coupling ESPResSo and waLBerla for scalable soft matter simulations with hydrodynamic interactions Tabular Data - 10.3 KB - 26 Variables, 54 Observations - UNF:6:kB4TJMITE1RCSTFxwc4KbA== Benchmark results for LB simulations with VTK writers (job_io.py).
asymmetric_charge.mp4 Feb 24, 2026 - Replication Data and Scripts for: Asymmetric Ionic Liquids for Enhanced Performance of Nanoporous Electrical Double Layer Capacitors Unknown - 11.3 MB - MD5: d16af1d1c5ba7aa6658df5c930d7d1df
asymmetric_size.mp4 Feb 24, 2026 - Replication Data and Scripts for: Asymmetric Ionic Liquids for Enhanced Performance of Nanoporous Electrical Double Layer Capacitors Unknown - 21.3 MB - MD5: 378985fde64f1e6a58e432d770b35df6
bulk_diffusion.py Feb 24, 2026 - Replication Data and Scripts for: Asymmetric Ionic Liquids for Enhanced Performance of Nanoporous Electrical Double Layer Capacitors Python Source Code - 10.1 KB - MD5: 4ad87a99c612051831e8e65479a3780b

espresso.patch

Apr 1, 2026 - Replication Data for: Coupling ESPResSo and waLBerla for scalable soft matter simulations with hydrodynamic interactions

Unknown - 2.7 KB -

Patch to improve ESPResSo's GPU algorithms performance on the MareNostrum 5 supercomputer.

espresso_build.sh

Apr 1, 2026 - Replication Data for: Coupling ESPResSo and waLBerla for scalable soft matter simulations with hydrodynamic interactions

Unknown - 3.0 KB -

Build commands for ESPResSo and waLBerla using the Nvidia HPC SDK 25.7 toolchain and the EESSI foss/2025a toolchain. Network connections to the MareNostrum supercomputer must be done through SSH, therefore the building of software requires extra steps.

espresso_fetch.sh

Apr 1, 2026 - Replication Data for: Coupling ESPResSo and waLBerla for scalable soft matter simulations with hydrodynamic interactions

Unknown - 793 B -

Download commands for the ESPResSo software and third-party libraries included via the CMake FetchContent mechanism. Network connections to the MareNostrum supercomputer must be done through SSH, therefore software must be downloaded locally and uploaded to the supercomputer via SSH.

python_build.sh

Apr 1, 2026 - Replication Data for: Coupling ESPResSo and waLBerla for scalable soft matter simulations with hydrodynamic interactions

Unknown - 448 B -

Build commands for Python, and virtual environment setup.

python_fetch.sh

Apr 1, 2026 - Replication Data for: Coupling ESPResSo and waLBerla for scalable soft matter simulations with hydrodynamic interactions

Unknown - 447 B -

Download commands for the Python source code and Python package wheels. Network connections to the MareNostrum supercomputer must be done through SSH, therefore software must be downloaded locally and uploaded to the supercomputer via SSH.

lb_gpu_coupling.tab

Apr 1, 2026 - Replication Data for: Coupling ESPResSo and waLBerla for scalable soft matter simulations with hydrodynamic interactions

Tabular Data - 10.3 KB - 22 Variables, 79 Observations -

Benchmark results for LB simulations with particle coupling (job_coupling.py).

lb_gpu_io.tab

Apr 1, 2026 - Replication Data for: Coupling ESPResSo and waLBerla for scalable soft matter simulations with hydrodynamic interactions

Tabular Data - 10.3 KB - 26 Variables, 54 Observations -

Benchmark results for LB simulations with VTK writers (job_io.py).

asymmetric_charge.mp4

Feb 24, 2026 - Replication Data and Scripts for: Asymmetric Ionic Liquids for Enhanced Performance of Nanoporous Electrical Double Layer Capacitors

Unknown - 11.3 MB -

asymmetric_size.mp4