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:9f-dn [version v1]

Publication date: Mar 08, 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 2023.39 (2023), https://doi.org/10.24435/materialscloud:9f-dn

Description

First-principles calculations of phonons are often based on the adiabatic approximation, and 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. A well-founded correction method exists [Phys. Rev. B 82, 165111 (2010)], which only relies on adiabatically screened quantities. However, many-body theory suggests to use one bare electron-phonon vertex in the phonon self-energy [Rev. Mod. Phys. 89, 015003 (2017)] to avoid double counting. We assess the accuracy of both approaches in estimating the low-temperature phonons of monolayer TaS₂ and doped MoS₂. We find that the former yields excellent results at low computational cost due to its designed error cancellation to first order, while the latter becomes exact in the many-body limit but is not accurate in approximate contexts. We offer a third strategy based on downfolding to partially screened phonons and interactions [Phys. Rev. B 92, 245108 (2015)] to keep both advantages. This is the natural scheme to include the electron-electron interaction and tackle phonons in strongly correlated materials and nonadiabatic renormalization 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 MD5md5:70499f843ea92442729fb2caea309527	7.0 KiB	Installation and usage instructions
qe2screen.patch MD5md5:6d6762f6910e250ac72ed75552986945	296.1 KiB	Quantum ESPRESSO source-code modifications
requirements.txt MD5md5:d9a4c00e608a3537bf4642d3df2ad4da	59 Bytes	List of Python dependencies
fig01.tar.gz MD5md5:c60c28dbaa8978c7b5f53bcc49692824	1.2 KiB	Python script and data to create Fig. 1
fig02.tar.gz MD5md5:5ccbcb726dc4d127e0cd1785f9011f0c	1.3 KiB	Python script and data to create Fig. 2
fig03.tar.gz MD5md5:a74c6d6f5f88f5810ddf663cd16c8543	346.9 KiB	Python script and data to create Fig. 3
fig04.tar.gz MD5md5:f4e270639430063f692d693c023e159f	117.4 KiB	Python script and data to create Fig. 4
fig05.tar.gz MD5md5:892b890550167c8199e42f2d3f18ba17	387.3 KiB	Python script and data to create Fig. 5
fig06.tar.gz MD5md5:156f23a5c536abecdce504c68d246135	31.5 KiB	Python script and data to create Fig. 6
fig07.tar.gz MD5md5:8fbc989f96ac45e408322b8479e68898	217.7 KiB	Python script and data to create Fig. 7
fig08.tar.gz MD5md5:ed653639bfe8151db1dc3fdcf16ffc4f	96.0 KiB	Python script and data to create Fig. 8
fig09.tar.gz MD5md5:d85033bd2de7ef362db358c3b557692d	55.9 KiB	Python script and data to create Fig. 9
fig10.tar.gz MD5md5:5c4cfe42cf5cff5e652013170510c229	228.8 KiB	Python script and data to create Fig. 10
fig11.tar.gz MD5md5:505577b648c8c496b6863d798e512d0e	149.2 KiB	Python script and data to create Fig. 11
fitQ.tar.gz MD5md5:cee0f6719bc9f2870867f6f7637fd4d1	3.3 KiB	Example of optimization of quadrupole tensors
input.tar.gz MD5md5:f0bbc234d05b8adeccb1aa9a9d5bdb09	3.3 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