DFT data of "Proximity-induced Cooper pairing at low and finite energies in the gold Rashba surface state" - Philipp Rüßmann, Masoud Bahari, Stefan Blügel, and Björn Trauzettel

This dataset contains the AiiDA export file for the DFT calculations on a Al/Au heterostructure where the superconducting proximity effect is investigated. Below are a list of the uuid's corresponding to the data discussed in the paper where it is discussed, and information on the used Python and AiiDA environment.

Figures in the main text

Normal state band structure in Figure 1

9d4ac8df-e169-4fdc-8ba5-e6cda30c9e98, band structure overview in Fig. 1a 74dd02a0-5d08-4786-8ca8-3a5c34a687cc, zoomed band structure with spin polarization in Fig. 1b

Band structures and DOS in Figure 2

fe4d2776-87dd-477f-ba82-a6cf551e79a6, band structure, no BdG 6efdfc87-bd77-475a-8554-2f5a418dc6bb, band structure, with BdG debba7bb-786a-429b-baa4-c44fb6a2c23e, DOS with BdG

Finite-energy pairings in Figure 3

18caf76f-3992-4ce9-97eb-1f5ed8842bb4, Fig. 3a 6efdfc87-bd77-475a-8554-2f5a418dc6bb, Fig. 3c

Band structure with external Zeeman field (B=2mRy) in Figure 6

ce5c2997-2f73-4a51-aaa2-f70f01f43fdc, Fig. 6a

Data and figures not in the main text

DOS in appendix

01e88fa4-0c9c-48d9-9163-562ca060afdc, DOS in appendix

Band structure for different lambda values (B=0)

e10505e2-147a-496c-9665-b8fceada5c46, lambda = 1.25 c58c774c-b41e-418d-b64f-0a7e7ffe3848, lambda = 1.30 d0979a07-5a25-4e17-8804-756c17abf38e, lambda = 1.35 3ea4b7e0-0a4e-4815-b2f5-bbeaa47ab175, lambda = 1.40 66b9973c-0d93-4f2a-ab81-0292cb0dd798, lambda = 1.45 c596ba66-4cc1-4ff9-abdd-f95a897b9e54, lambda = 1.50

Archive creation

The AiiDA export file was created using

verdi archive create -N 9d4ac8df-e169-4fdc-8ba5-e6cda30c9e98 \
    74dd02a0-5d08-4786-8ca8-3a5c34a687cc \
    fe4d2776-87dd-477f-ba82-a6cf551e79a6 \
    6efdfc87-bd77-475a-8554-2f5a418dc6bb \
    debba7bb-786a-429b-baa4-c44fb6a2c23e \
    18caf76f-3992-4ce9-97eb-1f5ed8842bb4 \
    6efdfc87-bd77-475a-8554-2f5a418dc6bb \
    ce5c2997-2f73-4a51-aaa2-f70f01f43fdc \
    01e88fa4-0c9c-48d9-9163-562ca060afdc \
    e10505e2-147a-496c-9665-b8fceada5c46 \
    c58c774c-b41e-418d-b64f-0a7e7ffe3848 \
    d0979a07-5a25-4e17-8804-756c17abf38e \
    3ea4b7e0-0a4e-4815-b2f5-bbeaa47ab175 \
    66b9973c-0d93-4f2a-ab81-0292cb0dd798 \
    c596ba66-4cc1-4ff9-abdd-f95a897b9e54 \
    --compress 9 -f export_Al_Au.aiida

Software environment

We used the following software stack:

• aiida-core version: AiiDA v2.3.0
• used Python environment:

$ pip freeze | grep 'aiida\|masci'
-e git+https://github.com/aiidateam/aiida-core@9e5f5eefd0cd6be44f0be76efae157dcf6e160ed#egg=aiida_core
-e git+https://github.com/JuDFTteam/aiida-fleur@52691acb5f19e25fd5726aca8ce5adb8db81dbb5#egg=aiida_fleur
-e git+https://iffgit.fz-juelich.de/aiida/aiida_nodes.git@9abe2bcd45c683d99f651b3573328e1fa5a6b8a9#egg=aiida_iffdata
-e git+https://github.com/JuDFTteam/aiida-jutools@1964cdd1695ebdd471b7baccd0a55fa6b3949312#egg=aiida_jutools
-e git+https://github.com/JuDFTteam/aiida-kkr@06e112ca0b7c9c021eba8084a83c8db05bded206#egg=aiida_kkr
-e git+https://github.com/JuDFTteam/aiida-spirit@d18975673dd9b8b52258d578c33ce08424469bbf#egg=aiida_spirit
-e git+https://github.com/JuDFTteam/masci-tools@8a3c606a6bf2122130d32d014e6d6f033f15830f#egg=masci_tools