Jan Berges^1*, Nina Girotto², Tim Wehling^3,4, Nicola Marzari^5,6,1, Samuel Poncé^7,6

1 U Bremen Excellence Chair, Bremen Center for Computational Materials Science, and MAPEX Center for Materials and Processes, University of Bremen, D-28359 Bremen, Germany

2 Institute of Physics, HR-10000 Zagreb, Croatia

3 I. Institute of Theoretical Physics, University of Hamburg, D-22607 Hamburg, Germany

4 The Hamburg Centre for Ultrafast Imaging, D-22761 Hamburg, Germany

5 Theory and Simulation of Materials (THEOS), École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland

6 National Centre for Computational Design and Discovery of Novel Materials (MARVEL), École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland

7 Institute of Condensed Matter and Nanosciences, Université catholique de Louvain, BE-1348 Louvain-la-Neuve, Belgium

* Corresponding authors emails: jan.berges@uni-bremen.de

DOI10.24435/materialscloud:dx-bw [version v2]

Publication date: Jul 03, 2023

How to cite this record

Jan Berges, Nina Girotto, Tim Wehling, Nicola Marzari, Samuel Poncé, Phonon self-energy corrections: To screen, or not to screen, Materials Cloud Archive 2023.103 (2023), https://doi.org/10.24435/materialscloud:dx-bw

Description

First-principles calculations of phonons are often based on the adiabatic approximation and on Brillouin-zone samplings that might not always be sufficient to capture the subtleties of Kohn anomalies. These shortcomings can be addressed through corrections to the phonon self-energy arising from the low-energy electrons. The exact self-energy involves a product of a bare and a screened electron-phonon vertex [Rev. Mod. Phys. 89, 015003 (2017)]; still, calculations often employ two adiabatically screened vertices, which have been proposed as a reliable approximation for self-energy differences [Phys. Rev. B 82, 165111 (2010)]. We assess the accuracy of both approaches in estimating the phonon spectral functions of model Hamiltonians and the adiabatic low-temperature phonon dispersions of monolayer TaS₂ and doped MoS₂. We find that the approximate method yields excellent corrections at low computational cost, due to its designed error cancellation to first order, while using a bare vertex could in principle improve these results but is challenging in practice. We offer an alternative strategy based on downfolding to partially screened phonons and interactions [Phys. Rev. B 92, 245108 (2015)]. This is a natural scheme to include electron-electron interactions and tackle phonons in strongly correlated materials and the frequency dependence of the electron-phonon vertex. This record contains (i) a patch for the PHonon and EPW codes of Quantum ESPRESSO, (ii) the Python scripts and data necessary to create all figures shown in our paper, (iii) a minimal working example of the optimization of quadrupole tensors, and (iv) the Quantum ESPRESSO input files we have used.

Materials Cloud sections using this data

Files

File name	Size	Description
README.md MD5md5:bd5246c0a01d1b772cb31aaded440585	7.1 KiB	Installation and usage instructions
qe2screen.patch MD5md5:f4db7efb11d99364ce27094d5dff6b4b	297.4 KiB	Quantum ESPRESSO source-code modifications
requirements.txt MD5md5:75461d5106cdab50b77520d9fa08024d	59 Bytes	List of Python dependencies
fig01.tar.gz MD5md5:230694522e1fc55762aceafcd3c93cfc	10.0 KiB	Python script and data to create Fig. 1
fig02.tar.gz MD5md5:2f5a62184e252983d3e4e02e9512de2e	20.0 KiB	Python script and data to create Fig. 2
fig03.tar.gz MD5md5:60388fe9b41a63abbeb5d7933d811e3d	1.3 MiB	Python script and data to create Fig. 3
fig04.tar.gz MD5md5:1c7e7e4bcf025c12105cbd6a463fd61f	630.0 KiB	Python script and data to create Fig. 4
fig05.tar.gz MD5md5:2106fe61935a55ffb0bcb2b1ac696a30	1.3 MiB	Python script and data to create Fig. 5
fig06.tar.gz MD5md5:f7435f52cf9ea335d1b276cd266e354f	170.0 KiB	Python script and data to create Fig. 6
fig07.tar.gz MD5md5:84937b5e64d4d091184b1dde0662b87e	930.0 KiB	Python script and data to create Fig. 7
fig08.tar.gz MD5md5:9173835fed40322e998dd3a9405b7b52	360.0 KiB	Python script and data to create Fig. 8
fig09.tar.gz MD5md5:c300086c9d922e59c6a176ee61d4737d	260.0 KiB	Python script and data to create Fig. 9
fig10.tar.gz MD5md5:684fb1301d3256a47d15bad584246169	1.2 MiB	Python script and data to create Fig. 10
fig11.tar.gz MD5md5:7ac41ff36b16a4eb0e50394d8bf68410	590.0 KiB	Python script and data to create Fig. 11
fig12.tar.gz MD5md5:d2b822720f3387f40ebb88df1205fab2	80.0 KiB	Python script and data to create Fig. 12
fitQ.tar.gz MD5md5:f378a5e62f2d6ac24d3b5cc25521b084	20.0 KiB	Example of optimization of quadrupole tensors
input.tar.gz MD5md5:ddddd231931de413535346d11949901a	40.0 KiB	Quantum ESPRESSO input files

License

Files and data are licensed under the terms of the following license: GNU General Public License v2.0 or later.
Metadata, except for email addresses, are licensed under the Creative Commons Attribution Share-Alike 4.0 International license.

External references

Keywords

MARVEL/DD3 SNSF H2020 PRACE electron-phonon coupling first principles phonons 2D materials