activity
20172021
most citedThe GW compendium: A practical guide to theoretical photoemission spectroscopy

433 citations · 709 across the 6 of their papers we have counts for

collaborators

10 papers

physics.chem-ph2021158 cited

Towards GW Calculations on Thousands of Atoms

Jan Wilhelm, Dorothea Golze, Leopold Talirz +2

The GW approximation of many-body perturbation theory is an accurate method for computing electron addition and removal energies of molecules and solids. In a canonical implementat…

physics.chem-ph202068 cited

Low-scaling with benchmark accuracy and application to phosphorene nanosheets

Jan Wilhelm, Patrick Seewald, Dorothea Golze

is an accurate method for computing electron addition and removal energies of molecules and solids. In a conventional implementation, however, its computational cost is $…

physics.chem-ph202033 cited

Relativistic correction scheme for core-level binding energies from

Levi Keller, Volker Blum, Patrick Rinke +1

We present a relativistic correction scheme to improve the accuracy of 1s core-level binding energies calculated from Green's function theory in the approximation, which does…

cond-mat.mtrl-sci2020

From flat to tilted: gradual interfaces in organic thin film growth

Laura Katharina Scarbath-Evers, René Hammer, Dorothea Golze +3

We investigate domain formation and local morphology of thin films of -sexithiophene (-6T) on Au(100) beyond monolayer coverage by combining high resolution scanning tunnelin…

physics.chem-ph2020

CP2K: An Electronic Structure and Molecular Dynamics Software Package -- Quickstep: Efficient and Accurate Electronic Structure Calculations

Thomas D. Kühne, Marcella Iannuzzi, Mauro Del Ben +36

CP2K is an open source electronic structure and molecular dynamics software package to perform atomistic simulations of solid-state, liquid, molecular and biological systems. It is…

physics.comp-ph2020

Atomic structures and orbital energies of 61,489 crystal-forming organic molecules

Annika Stuke, Christian Kunkel, Dorothea Golze +5

Data science and machine learning in materials science require large datasets of technologically relevant molecules or materials. Currently, publicly available molecular datasets w…