Shift current: Difference between revisions
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[[File:Sigma2.png|center| 600px | Shift current]] | [[File:Sigma2.png|center| 600px | Shift current]] | ||
Then we used this script to plot the result: | |||
import matplotlib.pyplot as plt | import matplotlib.pyplot as plt | ||
import numpy as np | import numpy as np |
Latest revision as of 14:50, 29 August 2024
This tutorial is for internal use only, these response functions are not implemented/tested in yambo/yambopy suite.
Introduction
In this tutorial we will show how to calculate Shift Current in bulk materials.
We suppose you are already familiar with the non-linear response using the Yambo code.
If it is not the case please study the previous tutorials:
Linear response using Dynamical Berry Phase and Real time approach to non-linear response (SHG).
Setup calculations
In this tutorial we will take as example the two dimensional hBN.
DFT wave-functions and inputs can be downloaded here: hBN-2D-RT.tar.gz.
First of all run the setup, then remove symmetries along the y direction, as explained in the tutorial above.
Real-time setup and calculations
In order to generate input file for shift current you do: yambo_nl -u n -V par
nloptics # [R] Non-linear spectroscopy NLogCPUs=0 # [PARALLEL] Live-timing CPU`s (0 for all) PAR_def_mode= "balanced" # [PARALLEL] Default distribution mode ("balanced"/"memory"/"workload"/"KQmemory") NL_CPU= " 8 1" # [PARALLEL] CPUs for each role NL_ROLEs= " w k" # [PARALLEL] CPUs roles (w,k) DIP_CPU= "" # [PARALLEL] CPUs for each role DIP_ROLEs= "" # [PARALLEL] CPUs roles (k,c,v) OSCLL_CPU= "" # [PARALLEL] CPUs for each role OSCLL_ROLEs= "" # [PARALLEL] CPUs roles (k,b) DIP_Threads=0 # [OPENMP/X] Number of threads for dipoles NL_Threads=0 # [OPENMP/NL] Number of threads for nl-optics OSCLL_Threads=0 # [OPENMP/X] Number of threads for Oscillators % NLBands 3 | 5 | # [NL] Bands range % NLverbosity= "high" # [NL] Verbosity level (low | high) NLtime=-1.000000 fs # [NL] Simulation Time NLintegrator= "INVINT" # [NL] Integrator ("EULEREXP/RK2/RK4/RK2EXP/HEUN/INVINT/CRANKNIC") NLCorrelation= "IPA" # [NL] Correlation ("IPA/HARTREE/TDDFT/LRC/LRW/JGM/SEX/LSEX/LHF") NLLrcAlpha= 0.000000 # [NL] Long Range Correction % NLEnRange 1.000000 | 8.000000 | eV # [NL] Energy range (for loop on frequencies NLEnSteps/=0 % NLEnSteps= 48 # [NL] Energy steps for the loop on frequencies % NLrotaxis 0.000000 | 0.000000 | 0.000000 | # [NL] Rotation axis (for the loop on angles NLAngSteps/=0) % NLAngSteps=0 # [NL] Angular steps (if NLAngSteps/=0 field versor will be ignored) NLDamping= 0.200000 eV # [NL] Damping (or dephasing) RADLifeTime=-1.000000 fs # [RT] Radiative life-time (if negative RADLifeTime=Phase_LifeTime) EvalCurrent # [NL] Evaluate the current #FrPolPerdic # [DIP] Force periodicity of polarization respect to the external field % Field1_Freq 0.100000 | 0.100000 | eV # [RT Field1] Frequency % Field1_NFreqs= 1 # [RT Field1] Frequency Field1_Int= 1000.00 kWLm2 # [RT Field1] Intensity Field1_Width= 0.000000 fs # [RT Field1] Width Field1_kind= "SOFTSIN" # [RT Field1] Kind(SIN|SOFTSIN| see more on src/modules/mod_fields.F) Field1_pol= "linear" # [RT Field1] Pol(linear|circular) % Field1_Dir 0.000000 | 1.000000 | 0.000000 | # [RT Field1] Versor % Field1_Tstart= 0.010000 fs # [RT Field1] Initial Time
Notice that in this input we turned one the evaluation of current EvalCurrent and force the parallelization on the frequencies, NL_CPU= " 8 1
, NL_ROLEs= " w k
, that is much more efficient than the one on k-points.
Analysis of the results
In order to analyze the result we use the branch shift_current of this Yambopy: https://github.com/attacc/yambopy/tree/shift_current,
and the following script for the post-processing:
from yambopy import * from yambopy.nl.harmonic_analysis import Harmonic_Analysis from yambopy.units import fs2aut X_order=4 NLDB=YamboNLDB() Harmonic_Analysis(NLDB,X_order=X_order,prn_Peff=True)
Then you can plot the file o.YamboPy-Sigma_probe_order_0
that contains the DC part of the current. In the figure below I plot the 5th column
that corresponds to the real part of [math]\displaystyle{ \sigma^{yyy} }[/math]:
Then we used this script to plot the result:
import matplotlib.pyplot as plt import numpy as np fig = plt.figure(figsize=(10,10)) ax = fig.add_subplot() plt.title("$|\sigma^{(2)}(0,\omega_1-\omega_1$)|",fontsize=20) fontsize2=14 XHI0 = np.genfromtxt('o.YamboPy-Sigma_probe_order_0',comments="#") plt.tick_params(\ axis='x', # changes apply to the x-axis which='both', # both major and minor ticks are affected bottom='off', # ticks along the bottom edge are off top='off', # ticks along the top edge are off labelbottom='off') plt.xlim(1.0,7.5) plt.xlabel('Energy [eV]',fontsize=20) plt.tick_params(axis='y', which='major', labelsize=14) # plt.plot(XHI0[:,0], XHI0[:,2], linewidth=2.5, linestyle="-",label='$\sigma^{(xyy)}$') plt.plot(XHI0[:,0], XHI0[:,4], linewidth=2.5, linestyle="-",label='Re $\sigma^{(yyy)}$') plt.axhline(y=0.0, color='black', linestyle='-') plt.legend(loc="upper right",fontsize=fontsize2) plt.xticks(fontsize=fontsize2) plt.savefig('sigma2.png', format='png') plt.show()