vasp.at

The Vienna Ab initio Simulation Package, better known as VASP, is a package for performing ab initio quantum mechanical calculations using either Vanderbilt pseudopotentials, or the projector augmented wave method, and a plane wave basis set. The basic methodology is density functional theory, but the code also allows use of post-DFT corrections such as hybrid functionals mixing DFT and Hartree–Fock exchange, many-body perturbation theory and dynamical electronic correlations within the random phase approximation. More information...

Multilingual Wikipedia

In June 2020 the website vasp.at was on the 245,698th place in the ranking of the most reliable and popular sources in multilingual Wikipedia from readers' point of view (PR-score). If we consider only frequency of appearance of this source in references of Wikipedia articles (F-score), this website was on the 570,890th place in June 2020. From Wikipedians' point of view, "vasp.at" is the 614,951st most reliable source in different language versions of Wikipedia (AR-score).

The website is placed before livestation.com and after potawatomilanguage.org in multilingual PR ranking of the most reliable sources in Wikipedia.

PR-score:
245,698th place
63,560
-190
AR-score:
614,951st place
2,910
+510
F-score:
570,890th place
8
+3

English Wikipedia (en)

PR-score:
156,981st place
53,010
-2,540
AR-score:
325,089th place
2,760
+510
F-score:
335,169th place
5
+3

Japanese Wikipedia (ja)

PR-score:
64,349th place
10,550
+2,350
AR-score:
154,948th place
150
0
F-score:
91,238th place
3
0

Popularity and reliability assessment of sources in references of Wikipedia in different languages. Data extraction based on complex method using Wikimedia dumps in July 2020. To find the most popular and reliable sources we used information about over 200 million references of Wikipedia articles. More details in the research "Modeling Popularity and Reliability of Sources in Multilingual Wikipedia". Values for PR-score and AR-score were additinaly increased 100 times (to distinguish smaller values in the ranking).