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Funding Information Agency: European Commission

1 to 10 of 49 Results

Code and benchmark for NPCS, a Native Provenance Computation for SPARQL Feb 21, 2024 - Analytic Computing Asma, Zubaria; Hernández, Daniel; Galárraga, Luis; Flouris, Giorgos; Fundulaki, Irini; Hose, Katja, 2024, "Code and benchmark for NPCS, a Native Provenance Computation for SPARQL", https://doi.org/10.18419/darus-3973, DaRUS, V1 Code for the implementation and benchmark of NPCS, a Native Provenance Computation for SPARQL. The code in this dataset includes the implementation of the NPCS system, which is a middleware for SPARQL endpoints that rewrites queries to queries that annotate answers with provenanc...
Code for: Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments Oct 15, 2021 - PN 6 Zaverkin, Viktor; Holzmüller, David; Steinwart, Ingo; Kästner, Johannes, 2021, "Code for: Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments", https://doi.org/10.18419/darus-2136, DaRUS, V1 Code and documentation for the improved Gaussian Moments Neural Network (GM-NN). An updated version can be found on GitLab
Data for "Absorbing State Phase Transition with Clifford Circuits" May 8, 2024 - Institute for Theoretical Physics III: Büchler Group Makki, Nastasia; Lang, Nicolai; Büchler, Hans Peter, 2024, "Data for "Absorbing State Phase Transition with Clifford Circuits"", https://doi.org/10.18419/darus-3369, DaRUS, V1 This dataset includes simulation data and jupyter notebooks used to reproduce the results in "Absorbing State Phase Transition with Clifford Circuits". The different folders correspond to figures of this paper. The notebooks are run with python 3 and include all functions used fo...
Data for: Dynamically stabilized phases with full ab initio accuracy: Thermodynamics of Ti, Zr, Hf with a focus on the hcp-bcc transition Dec 14, 2023 - Materials Design Jung, Jong Hyun; Forslund, Axel; Srinivasan, Prashanth; Grabowski, Blazej, 2023, "Data for: Dynamically stabilized phases with full ab initio accuracy: Thermodynamics of Ti, Zr, Hf with a focus on the hcp-bcc transition", https://doi.org/10.18419/darus-3582, DaRUS, V1, UNF:6:PcXLVWUQ0T4geRQy0F0sgg== [fileUNF] Data for the publication, Dynamically stabilized phases with full ab initio accuracy: Thermodynamics of Ti, Zr, Hf with a focus on the hcp-bcc transition, Phys. Rev. B 108, 184107 (2023). This data set contains 1) - the training sets (VASP files), - the low moment-tensor-potentia...
Data for: Electronic Moment Tensor Potentials include both electronic and vibrational degrees of freedom Mar 8, 2024 - Materials Design Srinivasan, Prashanth; Demuriya, David; Grabowski, Blazej; Shapeev, Alexander, 2024, "Data for: Electronic Moment Tensor Potentials include both electronic and vibrational degrees of freedom", https://doi.org/10.18419/darus-3891, DaRUS, V1 Data for "Srinivasan, P., Demuriya, D., Grabowski, B. et al. Electronic Moment Tensor Potentials include both electronic and vibrational degrees of freedom. npj Comput Mater 10, 41 (2024). doi:10.1038/s41524-024-01222-9 The dataset contains three folders: Data for the four figure...
Data for: High-accuracy thermodynamic properties to the melting point from ab initio calculations aided by machine-learning potentials Jan 11, 2023 - Materials Design Jung, Jong Hyun; Srinivasan, Prashanth; Forslund, Axel; Grabowski, Blazej, 2023, "Data for: High-accuracy thermodynamic properties to the melting point from ab initio calculations aided by machine-learning potentials", https://doi.org/10.18419/darus-3239, DaRUS, V1 Data for the publication High-accuracy thermodynamic properties to the melting point from ab initio calculations aided by machine-learning potentials, npj Comput. Mater., DOI: 10.1038/s41524-022-00956-8 (2023) This data set contains - the training sets (VASP files), - the low mom...
Data for: Thermodynamic properties on the homologous temperature scale from direct upsampling: Understanding electron-vibration coupling and thermal vacancies in bcc refractory metals May 12, 2023 - Materials Design Forslund, Axel; Jung, Jong Hyun; Srinivasan, Prashanth; Grabowski, Blazej, 2023, "Data for: Thermodynamic properties on the homologous temperature scale from direct upsampling: Understanding electron-vibration coupling and thermal vacancies in bcc refractory metals", https://doi.org/10.18419/darus-3339, DaRUS, V1 Data for the publication Thermodynamic properties on the homologous temperature scale from direct upsampling: Understanding electron-vibration coupling and thermal vacancies in bcc refractory metals, Phys. Rev. B 107, 174309 (2023). This data set contains - the training sets (VAS...
Data for: Transforming Laser-Scanned 750 kW Turbine Surface Geometry Data into Smooth CAD for CFD Simulations Feb 14, 2024 - Institute of Aerodynamics and Gas Dynamics Gagnon, Louis; Lutz, Thorsten, 2024, "Data for: Transforming Laser-Scanned 750 kW Turbine Surface Geometry Data into Smooth CAD for CFD Simulations", https://doi.org/10.18419/darus-3859, DaRUS, V1 Note for access: The data is available to anyone interested, but in order to monitor access, we ask that interested users request access by logging in by using the account of their academic institution, selecting the files they want, and clicking "Request Access" If you do not ha...
EHTM Code and Manual Oct 9, 2023 - Extended Hill-Type Muscle Material Model (EHTM) Nölle, Lennart; Lerge, Patrick; Martynenko, Oleksandr; Wochner, Isabell; Kempter, Fabian; Kleinbach, Christian; Schmitt, Syn; Fehr, Jörg, 2022, "EHTM Code and Manual", https://doi.org/10.18419/darus-1144, DaRUS, V2 This Dataset contains the implementation of the four element Extended Hill-type Muscle (EHTM) model with serial damping and eccentric force–velocity relation including Ca2+ dependent activation dynamics and internal methods for physiological muscle control for the finite-element...
exaFOAM Industrial Benchmark B2 - Unsteady primal and adjoint solution around the TME CHR car Mar 14, 2024 - HLRS-exaFOAM Margetis, Andreas-Stefanos; Papoutsis-Kiachagias, Evangelos; Giannakoglou, Kyriakos, 2024, "exaFOAM Industrial Benchmark B2 - Unsteady primal and adjoint solution around the TME CHR car", https://doi.org/10.18419/darus-3714, DaRUS, V1 This work is part of the exaFOAM project that aims to enable the open-source CFD software OpenFOAM to exploit massively parallel HPC architectures and overcome performance scaling bottlenecks. This benchmark represents an industrial application of the unsteady optimization infras...

Code and benchmark for NPCS, a Native Provenance Computation for SPARQL

Feb 21, 2024 - Analytic Computing

Asma, Zubaria; Hernández, Daniel; Galárraga, Luis; Flouris, Giorgos; Fundulaki, Irini; Hose, Katja, 2024, "Code and benchmark for NPCS, a Native Provenance Computation for SPARQL", https://doi.org/10.18419/darus-3973, DaRUS, V1

Code for the implementation and benchmark of NPCS, a Native Provenance Computation for SPARQL. The code in this dataset includes the implementation of the NPCS system, which is a middleware for SPARQL endpoints that rewrites queries to queries that annotate answers with provenanc...

Code for: Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments

Oct 15, 2021 - PN 6

Zaverkin, Viktor; Holzmüller, David; Steinwart, Ingo; Kästner, Johannes, 2021, "Code for: Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments", https://doi.org/10.18419/darus-2136, DaRUS, V1

Code and documentation for the improved Gaussian Moments Neural Network (GM-NN). An updated version can be found on GitLab

Data for "Absorbing State Phase Transition with Clifford Circuits"

May 8, 2024 - Institute for Theoretical Physics III: Büchler Group

Makki, Nastasia; Lang, Nicolai; Büchler, Hans Peter, 2024, "Data for "Absorbing State Phase Transition with Clifford Circuits"", https://doi.org/10.18419/darus-3369, DaRUS, V1

This dataset includes simulation data and jupyter notebooks used to reproduce the results in "Absorbing State Phase Transition with Clifford Circuits". The different folders correspond to figures of this paper. The notebooks are run with python 3 and include all functions used fo...

Data for: Dynamically stabilized phases with full ab initio accuracy: Thermodynamics of Ti, Zr, Hf with a focus on the hcp-bcc transition

Dec 14, 2023 - Materials Design

Jung, Jong Hyun; Forslund, Axel; Srinivasan, Prashanth; Grabowski, Blazej, 2023, "Data for: Dynamically stabilized phases with full ab initio accuracy: Thermodynamics of Ti, Zr, Hf with a focus on the hcp-bcc transition", https://doi.org/10.18419/darus-3582, DaRUS, V1, UNF:6:PcXLVWUQ0T4geRQy0F0sgg== [fileUNF]

Data for the publication, Dynamically stabilized phases with full ab initio accuracy: Thermodynamics of Ti, Zr, Hf with a focus on the hcp-bcc transition, Phys. Rev. B 108, 184107 (2023). This data set contains 1) - the training sets (VASP files), - the low moment-tensor-potentia...

Data for: Electronic Moment Tensor Potentials include both electronic and vibrational degrees of freedom

Mar 8, 2024 - Materials Design

Srinivasan, Prashanth; Demuriya, David; Grabowski, Blazej; Shapeev, Alexander, 2024, "Data for: Electronic Moment Tensor Potentials include both electronic and vibrational degrees of freedom", https://doi.org/10.18419/darus-3891, DaRUS, V1

Data for "Srinivasan, P., Demuriya, D., Grabowski, B. et al. Electronic Moment Tensor Potentials include both electronic and vibrational degrees of freedom. npj Comput Mater 10, 41 (2024). doi:10.1038/s41524-024-01222-9 The dataset contains three folders: Data for the four figure...

Data for: High-accuracy thermodynamic properties to the melting point from ab initio calculations aided by machine-learning potentials

Jan 11, 2023 - Materials Design

Jung, Jong Hyun; Srinivasan, Prashanth; Forslund, Axel; Grabowski, Blazej, 2023, "Data for: High-accuracy thermodynamic properties to the melting point from ab initio calculations aided by machine-learning potentials", https://doi.org/10.18419/darus-3239, DaRUS, V1

Data for the publication High-accuracy thermodynamic properties to the melting point from ab initio calculations aided by machine-learning potentials, npj Comput. Mater., DOI: 10.1038/s41524-022-00956-8 (2023) This data set contains - the training sets (VASP files), - the low mom...

Data for: Thermodynamic properties on the homologous temperature scale from direct upsampling: Understanding electron-vibration coupling and thermal vacancies in bcc refractory metals

May 12, 2023 - Materials Design

Forslund, Axel; Jung, Jong Hyun; Srinivasan, Prashanth; Grabowski, Blazej, 2023, "Data for: Thermodynamic properties on the homologous temperature scale from direct upsampling: Understanding electron-vibration coupling and thermal vacancies in bcc refractory metals", https://doi.org/10.18419/darus-3339, DaRUS, V1

Data for the publication Thermodynamic properties on the homologous temperature scale from direct upsampling: Understanding electron-vibration coupling and thermal vacancies in bcc refractory metals, Phys. Rev. B 107, 174309 (2023). This data set contains - the training sets (VAS...

Data for: Transforming Laser-Scanned 750 kW Turbine Surface Geometry Data into Smooth CAD for CFD Simulations

Feb 14, 2024 - Institute of Aerodynamics and Gas Dynamics

Gagnon, Louis; Lutz, Thorsten, 2024, "Data for: Transforming Laser-Scanned 750 kW Turbine Surface Geometry Data into Smooth CAD for CFD Simulations", https://doi.org/10.18419/darus-3859, DaRUS, V1

Note for access: The data is available to anyone interested, but in order to monitor access, we ask that interested users request access by logging in by using the account of their academic institution, selecting the files they want, and clicking "Request Access" If you do not ha...

EHTM Code and Manual

Oct 9, 2023 - Extended Hill-Type Muscle Material Model (EHTM)

Nölle, Lennart; Lerge, Patrick; Martynenko, Oleksandr; Wochner, Isabell; Kempter, Fabian; Kleinbach, Christian; Schmitt, Syn; Fehr, Jörg, 2022, "EHTM Code and Manual", https://doi.org/10.18419/darus-1144, DaRUS, V2

This Dataset contains the implementation of the four element Extended Hill-type Muscle (EHTM) model with serial damping and eccentric force–velocity relation including Ca2+ dependent activation dynamics and internal methods for physiological muscle control for the finite-element...

exaFOAM Industrial Benchmark B2 - Unsteady primal and adjoint solution around the TME CHR car

Mar 14, 2024 - HLRS-exaFOAM

Margetis, Andreas-Stefanos; Papoutsis-Kiachagias, Evangelos; Giannakoglou, Kyriakos, 2024, "exaFOAM Industrial Benchmark B2 - Unsteady primal and adjoint solution around the TME CHR car", https://doi.org/10.18419/darus-3714, DaRUS, V1

This work is part of the exaFOAM project that aims to enable the open-source CFD software OpenFOAM to exploit massively parallel HPC architectures and overcome performance scaling bottlenecks. This benchmark represents an industrial application of the unsteady optimization infras...

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