1 to 4 of 4 Results
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... |
Oct 11, 2023 - NMR insights into nanoconfined water using the surface exchange model
Gravelle, Simon, 2023, "Molecular simulation scripts for slit nanopores with tunable hydrophilicity", https://doi.org/10.18419/darus-3732, DaRUS, V1
GROMACS molecular simulation input files for slit nanopores filled with liquid water. Initial configuration can be generated using the Python script build_system.py. Use the bash script run_gmx.sh to run GROMACS. See the README.md file. |
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... |
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 |