Jekyll2025-12-01T21:27:14+00:00/feed.xmlMaterials Science Jupyter Notebooks2024-06-28T00:00:00+00:002024-06-28T00:00:00+00:00/2024/06/28/Charge_Density_Difference

Charge density difference

• Short script to obtain Charger density difference for two or more constituents , where

$$ \Delta \rho = \rho_{AB} - \rho_{A} - \rho_{B}$$

• This charge density difference is a grid file which can be visualized similiar to a CHGCAR file
• Moreover the planar averaged charge density is also plotted to visualize the transfer of electrons

[Pymatgen] (https://github.com/materialsproject/pymatgen) is used for loading and manuplating the CHGCAR files.

Raheel Hammad [ https://github.com/Sudo-Raheel ] (raheel.hammad1997@gmail.com)

# Uncomment the subsequent lines in this cell to install dependencies for Google Colab.
# !pip install pymatgen==2024.6.10

import os
from pymatgen.io.vasp.outputs import Chgcar, VolumetricData
from pymatgen.io.common import VolumetricData as CommonVolumetricData
import numpy as np

The working directory contains 3 CHGCAR files ... the total system {LiMoS2_F}(AB) and its constituent systems {LiMoS2}(A) and {G}(F)....

NOTE!!! The constituent systems A and B should not be optimized and it should be ensured that the real space grid NX, NY, NZ is the same for all CHGCAR files.

os.listdir()

['F_CHGCAR',
 'LiMoS2_F_CHGCAR',
 'LiMoS2_CHGCAR',
 '2024-06-25-Charge_Density_Difference.ipynb']

Read the CHGCAR files

the grid NX,NY,NZ is also printed

# Load the CHGCARs
chgcar_AB = Chgcar.from_file("chgcar_data/LiMoS2_F_CHGCAR")
chgcar_A = Chgcar.from_file("chgcar_data/LiMoS2_CHGCAR")
chgcar_B = Chgcar.from_file("chgcar_data/F_CHGCAR")

# Get the grid shape (same for all CHGCAR files)
print(np.shape(chgcar_B.data['total']))
grid=np.shape(chgcar_B.data['total'])

(54, 36, 192)

new CHGDIFF.vasp is created

poscar = chgcar_AB.poscar  # strucutre of total system 
data_diff = chgcar_AB.data['total'] - chgcar_A.data['total'] - chgcar_B.data['total']  # difference of grid data
del chgcar_AB, chgcar_A, chgcar_B 

#writing the new grid file
volumetric_diff = VolumetricData(structure=poscar.structure, data={'total': data_diff})
volumetric_diff.write_file('CHGDIFF.vasp')

VESTA visualization of CHGDIFF file

Planar averaged density

#planar averaged density
volumetric_diff = CommonVolumetricData(structure=poscar.structure, data={'total': data_diff})
planar_avg = volumetric_diff.get_average_along_axis(ind=2)  # ind 2 is for 001 direction 

c_latt=poscar.structure.lattice.c
planar_avg=planar_avg/c_latt  # linear charge density profile
c_arr=np.linspace(0,c_latt,grid[2])

Plotting the linear charge density

also the cumulative integral

from matplotlib import pyplot as plt 
from scipy.integrate import cumtrapz

desired_aspect_ratio = 1
fig_width = 8  # You can adjust this value to control the width of the figure
fig_height = fig_width / desired_aspect_ratio
fig, ax = plt.subplots(figsize=(fig_width, fig_height))

plt.xticks(fontweight='bold')
plt.yticks(fontsize=20, fontweight='bold')
plt.xticks(fontsize=20, fontweight='bold')
plt.tick_params(axis='x', which='both', length=4, width=2, pad=15)
plt.tick_params(axis='y', which='both', length=4, width=2)

ax.set_xlabel("Distance (Å)", color="black", fontsize=22, fontweight='bold')
ax.set_ylabel(r"$\mathregular {\int \Delta \rho (z) dz (e)}$", color="black", fontsize=26, fontweight='bold')
ax.set_xlim([0,35])

ax.set_xlim([0,35])

#cumulative integral 
integ_elec = cumtrapz(planar_avg, c_arr, initial=0)

ax.plot(c_arr,planar_avg,lw=2,color='purple',label='density')
ax.plot(c_arr, integ_elec, lw=2, color='black', ls='--',label='cumulative integral')
plt.legend(fontsize='12')

<matplotlib.legend.Legend at 0x7faee8380490>

Roughly 0.55 electrons have been transferred from $Li_1Mo_{16}S_{32}$ to the Flourine sheet.

]]>

2023-12-31T00:00:00+00:002023-12-31T00:00:00+00:00/2023/12/31/FHI-aims-example

from pathlib import Path
from subprocess import check_call

import numpy as np
from numpy.testing import assert_allclose

from pymatgen.core import Lattice, Structure
from pymatgen.io.aims.inputs import AimsControlIn, AimsCube, AimsGeometryIn
from pymatgen.io.aims.outputs import AimsOutput

# AIMS_CMD should be modified to match your system
AIMS_CMD = "aims.x"
AIMS_OUTPUT = "aims.out"
AIMS_SD = "species_dir"
AIMS_TEST_DIR = "../../tests/io/aims/species_directory/light/"

# Create test structure
structure = Structure(
    lattice=Lattice(np.array([[0, 2.715, 2.715], [2.715, 0, 2.715], [2.715, 2.715, 0]])),
    species=["Si", "Si"],
    coords=np.array([np.zeros(3), np.ones(3) * 0.25]),
)

# Create the geometry file from the structure
geo_in = AimsGeometryIn.from_structure(structure)

# Create the control.in file
cont_in = AimsControlIn(
    {
        "xc": "pw-lda",
        "relax_geometry": "trm 0.01",
        "relax_unit_cell": "full",
        "species_dir": AIMS_SD,
    }
)

# Add new parameters as if AimsControl
cont_in["k_grid"] = [1, 1, 1]

# Output options to control in automatically append the list
cont_in["output"] = "hirshfeld"
cont_in["output"] = ["eigenvectors"]

# Cube file output controlled by the AimsCube class
cont_in["cubes"] = [
    AimsCube("total_density", origin=[0, 0, 0], points=[11, 11, 11]),
    AimsCube("eigenstate_density 1", origin=[0, 0, 0], points=[11, 11, 11]),
]

# Write the input files
work_dir = Path.cwd() / "workdir/"
work_dir.mkdir(exist_ok=True)

geo_in.write_file(work_dir, overwrite=True)
cont_in.write_file(structure, work_dir, overwrite=True)

# Run the calculation
with open(f"{work_dir}/{AIMS_OUTPUT}", "w") as outfile:
    aims_run = check_call([AIMS_CMD], cwd=work_dir, stdout=outfile)

# Read the aims output file and the final relaxed geometry
outputs = AimsOutput.from_outfile(f"{work_dir}/{AIMS_OUTPUT}")
relaxed_structure = AimsGeometryIn.from_file(f"{work_dir}/geometry.in.next_step")

# Check the results
assert outputs.get_results_for_image(-1).lattice == relaxed_structure.structure.lattice
assert_allclose(outputs.get_results_for_image(-1).frac_coords, relaxed_structure.structure.frac_coords)
assert_allclose(outputs.get_results_for_image(-1).properties["stress"], outputs.stress)
assert_allclose(outputs.get_results_for_image(-1).site_properties["force"], outputs.forces)

]]>

2021-08-26T00:00:00+00:002021-08-26T00:00:00+00:00/2021/08/26/Magnetic%20Structure%20Generation%20as%20Input%20for%20Initial%20DFT%20Calculations

Magnetic Structure Generation as Input for Initial DFT Calculations

Author: Shivam Beniwal

Date: 26 August 2021

Introduction

This notebook demonstrates a helpful method from the pymatgen magnetism analyzer module to add magnetic information to a structure as an input to a DFT run where the outcome of the calculation is dependent on the initial magnetic structure, and/or requires some magnetic moment as starting point. This can be extremely useful in case of newly predicted structures.

# Uncomment the subsequent lines in this cell to install dependencies for Google Colab.
# !pip install pymatgen==2022.7.19

from pymatgen.analysis.magnetism.analyzer import CollinearMagneticStructureAnalyzer
from pymatgen.core.structure import Structure
from pymatgen.ext.matproj import MPRester
from pymatgen.symmetry.analyzer import SpacegroupAnalyzer

# Specify location of CIF on your device
structure = Structure.from_file("/home/user/Documents/cifs/lfp.cif")
print(structure)

Full Formula (Li14 Fe9 P16 O56)
Reduced Formula: Li14Fe9(P2O7)8
abc   :  10.747796   9.625861  12.724342
angles:  68.820392 116.025779 107.299876
Sites (95)
  #  SP           a         b         c
---  ----  --------  --------  --------
  0  Li+   0.137339  0.92805   0.597441
  1  Li+   0.126045  0.434026  0.0847
  2  Li+   0.626176  0.453868  0.590839
  3  Li+   0.373824  0.546132  0.409161
  4  Li+   0.021288  0.643996  0.39688
  5  Li+   0.862661  0.07195   0.402559
  6  Li+   0.978712  0.356004  0.60312
  7  Li+   0.497222  0.8674    0.316203
  8  Li+   0.514158  0.632938  0.173531
  9  Li+   0.62191   0.955426  0.089457
 10  Li+   0.485842  0.367062  0.826469
 11  Li+   0.37809   0.044574  0.910543
 12  Li+   0.502778  0.1326    0.683797
 13  Li+   0.873955  0.565974  0.9153
 14  Fe2+  0.744962  0.435377  0.3042
 15  Fe2+  0.264262  0.071531  0.201932
 16  Fe2+  0.217648  0.200486  0.416895
 17  Fe2+  0.735738  0.928469  0.798068
 18  Fe2+  0.255038  0.564623  0.6958
 19  Fe2+  0.782352  0.799514  0.583105
 20  Fe2+  0.780133  0.300909  0.07859
 21  Fe2+  0         0         0
 22  Fe2+  0.219867  0.699091  0.92141
 23  P5+   0.700902  0.638321  0.014979
 24  P5+   0.208131  0.231393  0.659702
 25  P5+   0.538911  0.196141  0.132759
 26  P5+   0.052823  0.968562  0.776985
 27  P5+   0.798275  0.263793  0.843927
 28  P5+   0.299098  0.361679  0.985021
 29  P5+   0.050175  0.469029  0.273158
 30  P5+   0.949825  0.530971  0.726842
 31  P5+   0.947177  0.031438  0.223015
 32  P5+   0.461089  0.803859  0.867241
 33  P5+   0.300562  0.864765  0.485466
 34  P5+   0.699438  0.135235  0.514534
 35  P5+   0.201725  0.736207  0.156073
 36  P5+   0.464356  0.308442  0.367888
 37  P5+   0.535644  0.691558  0.632112
 38  P5+   0.791869  0.768607  0.340298
 39  O2-   0.539863  0.158192  0.024743
 40  O2-   0.092099  0.652773  0.225938
 41  O2-   0.176637  0.901046  0.105233
 42  O2-   0.20505   0.21303   0.022179
 43  O2-   0.907901  0.347227  0.774062
 44  O2-   0.716621  0.007461  0.47944
 45  O2-   0.687158  0.754916  0.709086
 46  O2-   0.954695  0.45511   0.336803
 47  O2-   0.27639   0.492284  0.014508
 48  O2-   0.8474    0.341196  0.944993
 49  O2-   0.458339  0.562539  0.70479
 50  O2-   0.043629  0.050809  0.156863
 51  O2-   0.542044  0.165795  0.421783
 52  O2-   0.540547  0.662135  0.919652
 53  O2-   0.985828  0.405574  0.162943
 54  O2-   0.302814  0.399019  0.860061
 55  O2-   0.283379  0.992539  0.52056
 56  O2-   0.312842  0.245084  0.290914
 57  O2-   0.79495   0.78697   0.977821
 58  O2-   0.354953  0.203087  0.752629
 59  O2-   0.180042  0.392693  0.609766
 60  O2-   0.808738  0.58249   0.640002
 61  O2-   0.956371  0.949191  0.843137
 62  O2-   0.308298  0.743719  0.790965
 63  O2-   0.460137  0.841808  0.975257
 64  O2-   0.72361   0.507716  0.985492
 65  O2-   0.200766  0.718122  0.515678
 66  O2-   0.459453  0.337865  0.080348
 67  O2-   0.34922   0.712757  0.247493
 68  O2-   0.98317   0.904386  0.66758
 69  O2-   0.045305  0.54489   0.663197
 70  O2-   0.539787  0.65326   0.525967
 71  O2-   0.193456  0.914965  0.862184
 72  O2-   0.093481  0.150897  0.728314
 73  O2-   0.906519  0.849103  0.271686
 74  O2-   0.536855  0.934374  0.793678
 75  O2-   0.191262  0.41751   0.359998
 76  O2-   0.823363  0.098954  0.894767
 77  O2-   0.819958  0.607307  0.390234
 78  O2-   0.1526    0.658804  0.055007
 79  O2-   0.460213  0.34674   0.474033
 80  O2-   0.541661  0.437461  0.29521
 81  O2-   0.841986  0.849279  0.439014
 82  O2-   0.65078   0.287243  0.752507
 83  O2-   0.697186  0.600981  0.139939
 84  O2-   0.699212  0.094667  0.641348
 85  O2-   0.014173  0.594426  0.837057
 86  O2-   0.158014  0.150721  0.560986
 87  O2-   0.300788  0.905333  0.358652
 88  O2-   0.806544  0.085035  0.137816
 89  O2-   0.01683   0.095614  0.33242
 90  O2-   0.691702  0.256281  0.209035
 91  O2-   0.463145  0.065626  0.206322
 92  O2-   0.645047  0.796913  0.247371
 93  O2-   0.457956  0.834205  0.578217
 94  O2-   0.799234  0.281878  0.484322

Add magmoms to initial structure

Here we define magnetic moments of individual species present in the structure, if not already present. Refer to pymatgen docs for more information on options available for the argument overwrite_magmom_mode. Here we add magmoms for all sites in the structure irrespective of input structure, suitable for a spin-polarized (a.k.a 'magnetic') calculation.

This is particularly interesting in case of either attempting a ferromagnetic calculation or an antiferromagnetic calculation.

magmom = CollinearMagneticStructureAnalyzer(
    structure, overwrite_magmom_mode="replace_all_if_undefined"
)

# Assume an initial ferromagnetic order
fm_structure = magmom.get_ferromagnetic_structure(make_primitive=True)

print(fm_structure)

Full Formula (Li14 Fe9 P16 O56)
Reduced Formula: Li14Fe9(P2O7)8
abc   :  10.747796   9.625861  12.724342
angles:  68.820392 116.025779 107.299876
Sites (95)
  #  SP           a         b         c    magmom
---  ----  --------  --------  --------  --------
  0  Li+   0.137339  0.92805   0.597441         0
  1  Li+   0.126045  0.434026  0.0847           0
  2  Li+   0.626176  0.453868  0.590839         0
  3  Li+   0.373824  0.546132  0.409161         0
  4  Li+   0.021288  0.643996  0.39688          0
  5  Li+   0.862661  0.07195   0.402559         0
  6  Li+   0.978712  0.356004  0.60312          0
  7  Li+   0.497222  0.8674    0.316203         0
  8  Li+   0.514158  0.632938  0.173531         0
  9  Li+   0.62191   0.955426  0.089457         0
 10  Li+   0.485842  0.367062  0.826469         0
 11  Li+   0.37809   0.044574  0.910543         0
 12  Li+   0.502778  0.1326    0.683797         0
 13  Li+   0.873955  0.565974  0.9153           0
 14  Fe2+  0.744962  0.435377  0.3042           5
 15  Fe2+  0.264262  0.071531  0.201932         5
 16  Fe2+  0.217648  0.200486  0.416895         5
 17  Fe2+  0.735738  0.928469  0.798068         5
 18  Fe2+  0.255038  0.564623  0.6958           5
 19  Fe2+  0.782352  0.799514  0.583105         5
 20  Fe2+  0.780133  0.300909  0.07859          5
 21  Fe2+  0         0         0                5
 22  Fe2+  0.219867  0.699091  0.92141          5
 23  P5+   0.700902  0.638321  0.014979         0
 24  P5+   0.208131  0.231393  0.659702         0
 25  P5+   0.538911  0.196141  0.132759         0
 26  P5+   0.052823  0.968562  0.776985         0
 27  P5+   0.798275  0.263793  0.843927         0
 28  P5+   0.299098  0.361679  0.985021         0
 29  P5+   0.050175  0.469029  0.273158         0
 30  P5+   0.949825  0.530971  0.726842         0
 31  P5+   0.947177  0.031438  0.223015         0
 32  P5+   0.461089  0.803859  0.867241         0
 33  P5+   0.300562  0.864765  0.485466         0
 34  P5+   0.699438  0.135235  0.514534         0
 35  P5+   0.201725  0.736207  0.156073         0
 36  P5+   0.464356  0.308442  0.367888         0
 37  P5+   0.535644  0.691558  0.632112         0
 38  P5+   0.791869  0.768607  0.340298         0
 39  O2-   0.539863  0.158192  0.024743         0
 40  O2-   0.092099  0.652773  0.225938         0
 41  O2-   0.176637  0.901046  0.105233         0
 42  O2-   0.20505   0.21303   0.022179         0
 43  O2-   0.907901  0.347227  0.774062         0
 44  O2-   0.716621  0.007461  0.47944          0
 45  O2-   0.687158  0.754916  0.709086         0
 46  O2-   0.954695  0.45511   0.336803         0
 47  O2-   0.27639   0.492284  0.014508         0
 48  O2-   0.8474    0.341196  0.944993         0
 49  O2-   0.458339  0.562539  0.70479          0
 50  O2-   0.043629  0.050809  0.156863         0
 51  O2-   0.542044  0.165795  0.421783         0
 52  O2-   0.540547  0.662135  0.919652         0
 53  O2-   0.985828  0.405574  0.162943         0
 54  O2-   0.302814  0.399019  0.860061         0
 55  O2-   0.283379  0.992539  0.52056          0
 56  O2-   0.312842  0.245084  0.290914         0
 57  O2-   0.79495   0.78697   0.977821         0
 58  O2-   0.354953  0.203087  0.752629         0
 59  O2-   0.180042  0.392693  0.609766         0
 60  O2-   0.808738  0.58249   0.640002         0
 61  O2-   0.956371  0.949191  0.843137         0
 62  O2-   0.308298  0.743719  0.790965         0
 63  O2-   0.460137  0.841808  0.975257         0
 64  O2-   0.72361   0.507716  0.985492         0
 65  O2-   0.200766  0.718122  0.515678         0
 66  O2-   0.459453  0.337865  0.080348         0
 67  O2-   0.34922   0.712757  0.247493         0
 68  O2-   0.98317   0.904386  0.66758          0
 69  O2-   0.045305  0.54489   0.663197         0
 70  O2-   0.539787  0.65326   0.525967         0
 71  O2-   0.193456  0.914965  0.862184         0
 72  O2-   0.093481  0.150897  0.728314         0
 73  O2-   0.906519  0.849103  0.271686         0
 74  O2-   0.536855  0.934374  0.793678         0
 75  O2-   0.191262  0.41751   0.359998         0
 76  O2-   0.823363  0.098954  0.894767         0
 77  O2-   0.819958  0.607307  0.390234         0
 78  O2-   0.1526    0.658804  0.055007         0
 79  O2-   0.460213  0.34674   0.474033         0
 80  O2-   0.541661  0.437461  0.29521          0
 81  O2-   0.841986  0.849279  0.439014         0
 82  O2-   0.65078   0.287243  0.752507         0
 83  O2-   0.697186  0.600981  0.139939         0
 84  O2-   0.699212  0.094667  0.641348         0
 85  O2-   0.014173  0.594426  0.837057         0
 86  O2-   0.158014  0.150721  0.560986         0
 87  O2-   0.300788  0.905333  0.358652         0
 88  O2-   0.806544  0.085035  0.137816         0
 89  O2-   0.01683   0.095614  0.33242          0
 90  O2-   0.691702  0.256281  0.209035         0
 91  O2-   0.463145  0.065626  0.206322         0
 92  O2-   0.645047  0.796913  0.247371         0
 93  O2-   0.457956  0.834205  0.578217         0
 94  O2-   0.799234  0.281878  0.484322         0

order = magmom.ordering  # Useful if magnetic order is unknown or not user-defined
print(order)

Ordering.FM

Get space group information

spa = SpacegroupAnalyzer(structure)
spa.get_point_group_symbol()

'-1'

spa.get_space_group_symbol()

'P-1'

fm_structure.to(filename="lfp.mcif")  # Save the structure in magCIF format.

spn_structure = (
    magmom.get_structure_with_spin()
)  # Returns spin decorated values in structure instead of magmom site properties
print(spn_structure)

Full Formula (Li14 Fe9 P16 O56)
Reduced Formula: Li14Fe9(P2O7)8
abc   :  10.747796   9.625861  12.724342
angles:  68.820392 116.025779 107.299876
Sites (95)
  #  SP                  a         b         c
---  -----------  --------  --------  --------
  0  Li+,spin=0   0.137339  0.92805   0.597441
  1  Li+,spin=0   0.126045  0.434026  0.0847
  2  Li+,spin=0   0.626176  0.453868  0.590839
  3  Li+,spin=0   0.373824  0.546132  0.409161
  4  Li+,spin=0   0.021288  0.643996  0.39688
  5  Li+,spin=0   0.862661  0.07195   0.402559
  6  Li+,spin=0   0.978712  0.356004  0.60312
  7  Li+,spin=0   0.497222  0.8674    0.316203
  8  Li+,spin=0   0.514158  0.632938  0.173531
  9  Li+,spin=0   0.62191   0.955426  0.089457
 10  Li+,spin=0   0.485842  0.367062  0.826469
 11  Li+,spin=0   0.37809   0.044574  0.910543
 12  Li+,spin=0   0.502778  0.1326    0.683797
 13  Li+,spin=0   0.873955  0.565974  0.9153
 14  Fe2+,spin=5  0.744962  0.435377  0.3042
 15  Fe2+,spin=5  0.264262  0.071531  0.201932
 16  Fe2+,spin=5  0.217648  0.200486  0.416895
 17  Fe2+,spin=5  0.735738  0.928469  0.798068
 18  Fe2+,spin=5  0.255038  0.564623  0.6958
 19  Fe2+,spin=5  0.782352  0.799514  0.583105
 20  Fe2+,spin=5  0.780133  0.300909  0.07859
 21  Fe2+,spin=5  0         0         0
 22  Fe2+,spin=5  0.219867  0.699091  0.92141
 23  P5+,spin=0   0.700902  0.638321  0.014979
 24  P5+,spin=0   0.208131  0.231393  0.659702
 25  P5+,spin=0   0.538911  0.196141  0.132759
 26  P5+,spin=0   0.052823  0.968562  0.776985
 27  P5+,spin=0   0.798275  0.263793  0.843927
 28  P5+,spin=0   0.299098  0.361679  0.985021
 29  P5+,spin=0   0.050175  0.469029  0.273158
 30  P5+,spin=0   0.949825  0.530971  0.726842
 31  P5+,spin=0   0.947177  0.031438  0.223015
 32  P5+,spin=0   0.461089  0.803859  0.867241
 33  P5+,spin=0   0.300562  0.864765  0.485466
 34  P5+,spin=0   0.699438  0.135235  0.514534
 35  P5+,spin=0   0.201725  0.736207  0.156073
 36  P5+,spin=0   0.464356  0.308442  0.367888
 37  P5+,spin=0   0.535644  0.691558  0.632112
 38  P5+,spin=0   0.791869  0.768607  0.340298
 39  O2-,spin=0   0.539863  0.158192  0.024743
 40  O2-,spin=0   0.092099  0.652773  0.225938
 41  O2-,spin=0   0.176637  0.901046  0.105233
 42  O2-,spin=0   0.20505   0.21303   0.022179
 43  O2-,spin=0   0.907901  0.347227  0.774062
 44  O2-,spin=0   0.716621  0.007461  0.47944
 45  O2-,spin=0   0.687158  0.754916  0.709086
 46  O2-,spin=0   0.954695  0.45511   0.336803
 47  O2-,spin=0   0.27639   0.492284  0.014508
 48  O2-,spin=0   0.8474    0.341196  0.944993
 49  O2-,spin=0   0.458339  0.562539  0.70479
 50  O2-,spin=0   0.043629  0.050809  0.156863
 51  O2-,spin=0   0.542044  0.165795  0.421783
 52  O2-,spin=0   0.540547  0.662135  0.919652
 53  O2-,spin=0   0.985828  0.405574  0.162943
 54  O2-,spin=0   0.302814  0.399019  0.860061
 55  O2-,spin=0   0.283379  0.992539  0.52056
 56  O2-,spin=0   0.312842  0.245084  0.290914
 57  O2-,spin=0   0.79495   0.78697   0.977821
 58  O2-,spin=0   0.354953  0.203087  0.752629
 59  O2-,spin=0   0.180042  0.392693  0.609766
 60  O2-,spin=0   0.808738  0.58249   0.640002
 61  O2-,spin=0   0.956371  0.949191  0.843137
 62  O2-,spin=0   0.308298  0.743719  0.790965
 63  O2-,spin=0   0.460137  0.841808  0.975257
 64  O2-,spin=0   0.72361   0.507716  0.985492
 65  O2-,spin=0   0.200766  0.718122  0.515678
 66  O2-,spin=0   0.459453  0.337865  0.080348
 67  O2-,spin=0   0.34922   0.712757  0.247493
 68  O2-,spin=0   0.98317   0.904386  0.66758
 69  O2-,spin=0   0.045305  0.54489   0.663197
 70  O2-,spin=0   0.539787  0.65326   0.525967
 71  O2-,spin=0   0.193456  0.914965  0.862184
 72  O2-,spin=0   0.093481  0.150897  0.728314
 73  O2-,spin=0   0.906519  0.849103  0.271686
 74  O2-,spin=0   0.536855  0.934374  0.793678
 75  O2-,spin=0   0.191262  0.41751   0.359998
 76  O2-,spin=0   0.823363  0.098954  0.894767
 77  O2-,spin=0   0.819958  0.607307  0.390234
 78  O2-,spin=0   0.1526    0.658804  0.055007
 79  O2-,spin=0   0.460213  0.34674   0.474033
 80  O2-,spin=0   0.541661  0.437461  0.29521
 81  O2-,spin=0   0.841986  0.849279  0.439014
 82  O2-,spin=0   0.65078   0.287243  0.752507
 83  O2-,spin=0   0.697186  0.600981  0.139939
 84  O2-,spin=0   0.699212  0.094667  0.641348
 85  O2-,spin=0   0.014173  0.594426  0.837057
 86  O2-,spin=0   0.158014  0.150721  0.560986
 87  O2-,spin=0   0.300788  0.905333  0.358652
 88  O2-,spin=0   0.806544  0.085035  0.137816
 89  O2-,spin=0   0.01683   0.095614  0.33242
 90  O2-,spin=0   0.691702  0.256281  0.209035
 91  O2-,spin=0   0.463145  0.065626  0.206322
 92  O2-,spin=0   0.645047  0.796913  0.247371
 93  O2-,spin=0   0.457956  0.834205  0.578217
 94  O2-,spin=0   0.799234  0.281878  0.484322

The above structure is saved as a magCIF with .mcif extension. This can be converted back to a CIF with relevant magnetic information associated with each site. OpenBabel does this easily, on command line write-

obabel -imcif lfp.mcif -ocif -O lfp.cif

Analyze magnetic moment present in a calculated structure using MAPI

In some cases, it might be useful to analyze magnetic behavior of a strucure from the Materials Project database.

# Establish rester for accessing Materials API
mpr = MPRester(api_key="API_KEY")
mp_id = "mp-504263"  # Previously reported structure; Co replaced at Fe site
structure_from_mp = mpr.get_structure_by_material_id(mp_id)
print(structure)

Full Formula (Li14 Co9 P16 O56)
Reduced Formula: Li14Co9(P2O7)8
abc   :  10.747796   9.625861  12.724342
angles:  68.820392 116.025779 107.299876
Sites (95)
  #  SP           a         b         c    magmom
---  ----  --------  --------  --------  --------
  0  Li    0.137339  0.92805   0.597441     0
  1  Li    0.126045  0.434026  0.0847       0
  2  Li    0.626176  0.453868  0.590839     0.001
  3  Li    0.373824  0.546132  0.409161     0.001
  4  Li    0.021288  0.643996  0.39688      0
  5  Li    0.862661  0.07195   0.402559     0
  6  Li    0.978712  0.356004  0.60312      0
  7  Li    0.497222  0.8674    0.316203    -0
  8  Li    0.514158  0.632938  0.173531    -0
  9  Li    0.62191   0.955426  0.089457     0.001
 10  Li    0.485842  0.367062  0.826469    -0
 11  Li    0.37809   0.044574  0.910543     0.001
 12  Li    0.502778  0.1326    0.683797    -0
 13  Li    0.873955  0.565974  0.9153       0
 14  Co    0.744962  0.435377  0.3042       2.768
 15  Co    0.264262  0.071531  0.201932     2.762
 16  Co    0.217648  0.200486  0.416895     2.746
 17  Co    0.735738  0.928469  0.798068     2.762
 18  Co    0.255038  0.564623  0.6958       2.768
 19  Co    0.782352  0.799514  0.583105     2.747
 20  Co    0.780133  0.300909  0.07859      2.739
 21  Co    0         0         0           -0.988
 22  Co    0.219867  0.699091  0.92141      2.739
 23  P     0.700902  0.638321  0.014979     0.007
 24  P     0.208131  0.231393  0.659702     0.004
 25  P     0.538911  0.196141  0.132759     0.003
 26  P     0.052823  0.968562  0.776985     0.009
 27  P     0.798275  0.263793  0.843927     0.004
 28  P     0.299098  0.361679  0.985021     0.007
 29  P     0.050175  0.469029  0.273158     0.007
 30  P     0.949825  0.530971  0.726842     0.009
 31  P     0.947177  0.031438  0.223015     0.008
 32  P     0.461089  0.803859  0.867241     0.003
 33  P     0.300562  0.864765  0.485466     0.008
 34  P     0.699438  0.135235  0.514534     0.009
 35  P     0.201725  0.736207  0.156073     0.004
 36  P     0.464356  0.308442  0.367888     0.004
 37  P     0.535644  0.691558  0.632112     0.004
 38  P     0.791869  0.768607  0.340298     0.006
 39  O     0.539863  0.158192  0.024743     0.024
 40  O     0.092099  0.652773  0.225938     0.007
 41  O     0.176637  0.901046  0.105233     0.036
 42  O     0.20505   0.21303   0.022179     0.018
 43  O     0.907901  0.347227  0.774062     0.008
 44  O     0.716621  0.007461  0.47944      0.025
 45  O     0.687158  0.754916  0.709086     0.053
 46  O     0.954695  0.45511   0.336803     0.025
 47  O     0.27639   0.492284  0.014508     0.026
 48  O     0.8474    0.341196  0.944993     0.032
 49  O     0.458339  0.562539  0.70479      0.025
 50  O     0.043629  0.050809  0.156863     0.029
 51  O     0.542044  0.165795  0.421783     0.003
 52  O     0.540547  0.662135  0.919652     0.002
 53  O     0.985828  0.405574  0.162943     0.026
 54  O     0.302814  0.399019  0.860061     0.026
 55  O     0.283379  0.992539  0.52056      0.024
 56  O     0.312842  0.245084  0.290914     0.053
 57  O     0.79495   0.78697   0.977821     0.018
 58  O     0.354953  0.203087  0.752629     0.001
 59  O     0.180042  0.392693  0.609766     0.03
 60  O     0.808738  0.58249   0.640002     0.037
 61  O     0.956371  0.949191  0.843137     0.029
 62  O     0.308298  0.743719  0.790965     0.05
 63  O     0.460137  0.841808  0.975257     0.024
 64  O     0.72361   0.507716  0.985492     0.026
 65  O     0.200766  0.718122  0.515678     0.026
 66  O     0.459453  0.337865  0.080348     0.002
 67  O     0.34922   0.712757  0.247493     0.002
 68  O     0.98317   0.904386  0.66758      0.026
 69  O     0.045305  0.54489   0.663197     0.023
 70  O     0.539787  0.65326   0.525967     0.024
 71  O     0.193456  0.914965  0.862184     0.036
 72  O     0.093481  0.150897  0.728314     0.008
 73  O     0.906519  0.849103  0.271686     0.008
 74  O     0.536855  0.934374  0.793678     0.026
 75  O     0.191262  0.41751   0.359998     0.037
 76  O     0.823363  0.098954  0.894767     0.036
 77  O     0.819958  0.607307  0.390234     0.028
 78  O     0.1526    0.658804  0.055007     0.032
 79  O     0.460213  0.34674   0.474033     0.021
 80  O     0.541661  0.437461  0.29521      0.023
 81  O     0.841986  0.849279  0.439014     0.03
 82  O     0.65078   0.287243  0.752507     0.002
 83  O     0.697186  0.600981  0.139939     0.028
 84  O     0.699212  0.094667  0.641348     0.027
 85  O     0.014173  0.594426  0.837057     0.026
 86  O     0.158014  0.150721  0.560986     0.034
 87  O     0.300788  0.905333  0.358652     0.027
 88  O     0.806544  0.085035  0.137816     0.036
 89  O     0.01683   0.095614  0.33242      0.026
 90  O     0.691702  0.256281  0.209035     0.05
 91  O     0.463145  0.065626  0.206322     0.026
 92  O     0.645047  0.796913  0.247371     0.001
 93  O     0.457956  0.834205  0.578217     0.003
 94  O     0.799234  0.281878  0.484322     0.026

mgmmnt = CollinearMagneticStructureAnalyzer(
    structure_from_mp, overwrite_magmom_mode="replace_all_if_undefined"
)

mgmmnt.is_magnetic

True

mgmmnt.magnetic_species_and_magmoms

{'Co': [0.988, 2.739, 2.746, 2.747, 2.762, 2.768]}

mgmmnt.ordering

<Ordering.FiM: 'FiM'>

]]>

2021-05-12T00:00:00+00:002021-05-12T00:00:00+00:00/2021/05/12/Explanation%20of%20Corrections

Demonstration of Materials Project Energy Corrections

This notebook illustrates how to apply and obtain an explanation of energy corrections used in the Materials Project database.

Author: Ryan Kingsbury

Date: May 2021

Overview

The Materials Project API (MPRester) returns ComputedEntry objects that contain information about DFT calculations. By default, these objects have adjustments applied to the energies of certain elements to reduce certain systematic errors. See our documentation for complete details.

As of Spring 2021, ComputedEntry are processed using the MaterialsProject2020Compatibility class in pymatgen by default. The legacy correction scheme, used from 2010-2020, is still available in MaterialsProjectCompatibility.

# Uncomment the subsequent lines in this cell to install dependencies for Google Colab.
# !pip install pymatgen==2022.7.19

from pymatgen.entries.compatibility import (
    MaterialsProject2020Compatibility,
    MaterialsProjectCompatibility,
)
from pymatgen.ext.matproj import MPRester

Default behavior - MaterialsProject2020Compatibility

Let's retrieve entries in the Cl-Mo-O system to demonstrate how this works.

# retrieve
with MPRester() as m:
    entries = m.get_entries_in_chemsys("Cl-Mo-O")
entry = entries[0]

You can examine the energy corrections via the energy_adjustments attribute of the ComputedEntry. This attribute contains a list of each energy correction that has been applied.

entries[25].energy_adjustments

[]

If you want even more detail, you can examine an indiviual EnergyAdjustment (one element of the list)

Applying the legacy corrections with MaterialsProjectCompatibility

If you want to use the old corrections, or apply your own, you can re-process the ComputedEntry obtained from MPRester using a Compatibility class. The .process_entries method will remove any previously-applied energy corrections and re-process the entry in-place.

compat = MaterialsProjectCompatibility()

entries = compat.process_entries(entries)

entries[25].energy_adjustments

[ConstantEnergyAdjustment:
   Name: MP Anion Correction
   Value: -2.809 eV
   Uncertainty: nan eV
   Description: Constant energy adjustment (-2.809 eV)
   Generated by: MaterialsProjectCompatibility,
 ConstantEnergyAdjustment:
   Name: MP Advanced Correction
   Value: -28.248 eV
   Uncertainty: nan eV
   Description: Constant energy adjustment (-28.248 eV)
   Generated by: MaterialsProjectCompatibility]

Notice how the energy adjustments have changed. The class name, description and values are all different. You will also notice that the descriptions of the legacy corrections are less verbose than those of the modern MaterialsProject2020Compatibility corrections.

Removing corrections altogther

If you want to remove all corrections from a ComputedEntry, simply set energy_adjustments to an empty list. You can verify that you have removed corrections by checking the energy_per_atom and the correction_per_atom of the ComputedEntry before and after.

entries[25].energy_per_atom

-4.639152145227273

entries[25].correction_per_atom

-0.7058445454545454

entries[25].energy_adjustments = []
entries[25].energy_per_atom

-3.9333075997727276

entries[25].correction_per_atom

0.0

Alternatively, you can simply pass compatible_only=False to the MPRester call when you download data.

# retrieve
with MPRester() as m:
    entries = m.get_entries_in_chemsys("Cl-Mo-O", compatible_only=False)
entry = entries[0]

entries[25].energy_adjustments

[]

]]>

2020-07-15T00:00:00+00:002020-07-15T00:00:00+00:00/2020/07/15/How%20to%20plot%20a%20Fermi%20surface

How to plot a Fermi surface

Using BoltzTraP v1

PbTe mp-19717

# Uncomment the subsequent lines in this cell to install dependencies for Google Colab.
# !pip install pymatgen==2022.7.19

import os

from pymatgen.electronic_structure.plotter import plot_fermi_surface
from pymatgen.symmetry.bandstructure import HighSymmKpath

vrun = Vasprun("boltztrap2_data/vasprun-PbTe_uniform_bs.xml")
bs = vrun.get_band_structure()
nelect = vrun.parameters["NELECT"]

/usr/lib64/python3.7/site-packages/pymatgen/io/vasp/outputs.py:886: UserWarning: No POTCAR file with matching TITEL fields was found in /home/tardo/github/matgenb/notebooks/boltztrap2_data
  " was found in {}".format(os.path.abspath(p)))

VBM

# specify here the band index you need, in this example the vbm is taken
vbm = bs.get_vbm()["band_index"][Spin.up][-1]

# create the directory "fermi/vbm" before
os.makedirs("fermi/vbm/", exist_ok=True)

# run the interpolation, lpfac should be between 50 and 150
BoltztrapRunner(
    bs=bs, nelec=nelect, lpfac=10, run_type="FERMI", band_nb=vbm, cond_band=False
).run(path_dir="fermi/vbm/")

# read the output
an = BoltztrapAnalyzer.from_files("fermi/vbm/boltztrap/")

# prepare high symmetry points labels
st = vrun.final_structure
kpoints_dict = HighSymmKpath(st).kpath["kpoints"]

# edit or generete a dict like this one to pass the kpoint labels and frac coords you want to plot
kpoints_dict

{'\\Gamma': array([0., 0., 0.]),
 'K': array([0.375, 0.375, 0.75 ]),
 'L': array([0.5, 0.5, 0.5]),
 'U': array([0.625, 0.25 , 0.625]),
 'W': array([0.5 , 0.25, 0.75]),
 'X': array([0.5, 0. , 0.5])}

an.fermi_surface_data.max()

0.005432916487335469

# plot the surface in an external window
# vbm is set at 0 eV, so we set an energy level of 0.05 below the vbm
plot_fermi_surface(
    an.fermi_surface_data,
    bs.structure,
    cbm=False,
    energy_levels=[-0.05],
    kpoints_dict=kpoints_dict,
    labels_scale_factor=0.1,
)

(<mayavi.core.scene.Scene at 0x7fcf4f4737d0>,
 <module 'mayavi.mlab' from '/home/tardo/.local/lib/python3.7/site-packages/mayavi/mlab.py'>)

CBM

# specify here another band index you need, in this example the cbm is taken
cbm = bs.get_cbm()["band_index"][Spin.up][0]

# create the directory "fermi/cbm" before
os.makedirs("fermi/cbm/", exist_ok=True)

# run the interpolation, lpfac should be between 50 and 150
BoltztrapRunner(
    bs=bs, nelec=nelect, lpfac=10, run_type="FERMI", band_nb=cbm, cond_band=True
).run(path_dir="fermi/cbm/")

# read the output
an = BoltztrapAnalyzer.from_files("fermi/cbm/boltztrap/")

an.fermi_surface_data.max()

-0.8262383617146892

# plot the surface in an external window
# cbm is at ~0.83 eV, so we set an energy level of 0.07 above the cbm

plot_fermi_surface(
    an.fermi_surface_data,
    bs.structure,
    cbm=True,
    energy_levels=[0.9],
    kpoints_dict=kpoints_dict,
    labels_scale_factor=0.1,
)

(<mayavi.core.scene.Scene at 0x7ffae8070350>,
 <module 'mayavi.mlab' from '/home/tardo/.local/lib/python3.7/site-packages/mayavi/mlab.py'>)

AlAs mp-2172

Example of how to plot multiple surfaces in the same figure

vrun = Vasprun("boltztrap2_data/vasprun_AlAs.xml.gz")
bs = vrun.get_band_structure()
nelect = vrun.parameters["NELECT"]

/usr/lib64/python3.7/site-packages/pymatgen/io/vasp/outputs.py:886: UserWarning: No POTCAR file with matching TITEL fields was found in /home/tardo/github/matgenb/notebooks/boltztrap2_data
  " was found in {}".format(os.path.abspath(p)))

cbm = bs.get_cbm()["band_index"][Spin.up][0]
vb1 = bs.get_vbm()["band_index"][Spin.up][-1]
vb2 = bs.get_vbm()["band_index"][Spin.up][-2]
vb3 = bs.get_vbm()["band_index"][Spin.up][-3]

cbm, vb1, vb2, vb3

(4, 3, 2, 1)

st = vrun.final_structure
kpoints_dict = HighSymmKpath(st).kpath["kpoints"]

kpoints_dict

{'\\Gamma': array([0., 0., 0.]),
 'K': array([0.375, 0.375, 0.75 ]),
 'L': array([0.5, 0.5, 0.5]),
 'U': array([0.625, 0.25 , 0.625]),
 'W': array([0.5 , 0.25, 0.75]),
 'X': array([0.5, 0. , 0.5])}

CBM

os.makedirs("fermi/cbm/", exist_ok=True)

# run the interpolation, lpfac should be between 50 and 150
BoltztrapRunner(
    bs=bs, nelec=nelect, lpfac=5, run_type="FERMI", band_nb=cbm, cond_band=True
).run(path_dir="fermi/cbm/")

# read the output
an = BoltztrapAnalyzer.from_files("fermi/cbm/boltztrap/")

# plot the fermi surface and get the figure object
fig, mlab = plot_fermi_surface(
    an.fermi_surface_data,
    bs.structure,
    cbm=True,
    energy_levels=[1.55],
    kpoints_dict=kpoints_dict,
    labels_scale_factor=0.1,
    interative=False,
    multiple_figure=False,
    color=(1, 0, 0),
)

First three vb

for vb, tf in zip((vb1, vb2, vb3), (0.2, 0.4, 1)):
    os.makedirs(f"fermi/vb_{vb}", exist_ok=True)
    # run the interpolation, lpfac should be between 50 and 150
    BoltztrapRunner(
        bs=bs, nelec=nelect, lpfac=50, run_type="FERMI", band_nb=vb, cond_band=False
    ).run(path_dir=f"fermi/vb_{vb}/")

    # read the output
    an = BoltztrapAnalyzer.from_files(f"fermi/vb_{vb}/boltztrap/")

    # plot the fermi surface in the previous figure object
    fig, mlab = plot_fermi_surface(
        an.fermi_surface_data,
        bs.structure,
        cbm=False,
        energy_levels=[-0.07],
        kpoints_dict=kpoints_dict,
        labels_scale_factor=0.1,
        interative=False,
        mlab_figure=fig,
        multiple_figure=False,
        transparency_factor=[tf],
    )
mlab.show()

3
2
1

In case of a cube file from other software

from pymatgen.electronic_structure.boltztrap import read_cube_file

# decompress boltztrap_BZ.cube.gz before running the following
fs_data = read_cube_file("boltztrap2_data/boltztrap_BZ.cube")
st = Vasprun("boltztrap2_data/vasprun_mp-12103.xml.gz").final_structure
kpoints_dict = HighSymmKpath(st).kpath["kpoints"]

/usr/lib64/python3.7/site-packages/pymatgen/io/vasp/outputs.py:886: UserWarning: No POTCAR file with matching TITEL fields was found in /home/tardo/github/matgenb/notebooks/boltztrap2_data
  " was found in {}".format(os.path.abspath(p)))

plot_fermi_surface(
    fs_data,
    st,
    cbm=True,
    kpoints_dict=kpoints_dict,
    labels_scale_factor=0.1,
    color=(1, 0, 0),
)

Energy level set to: 1.0449469713543411 eV

(<mayavi.core.scene.Scene at 0x7ffaccb68b30>,
 <module 'mayavi.mlab' from '/home/tardo/.local/lib/python3.7/site-packages/mayavi/mlab.py'>)

Using BoltzTraP 2 pymatgen interface

from pymatgen.electronic_structure.boltztrap2 import BztInterpolator, VasprunBSLoader

vrun = Vasprun("boltztrap2_data/vasprun-PbTe_uniform_bs.xml")
bs = vrun.get_band_structure()
nelect = vrun.parameters["NELECT"]

/usr/lib64/python3.7/site-packages/pymatgen/io/vasp/outputs.py:886: UserWarning: No POTCAR file with matching TITEL fields was found in /home/tardo/github/matgenb/notebooks/boltztrap2_data
  " was found in {}".format(os.path.abspath(p)))

data = VasprunBSLoader(vrun)

bztI = BztInterpolator(data, lpfac=10)

bztI.eband.shape

(6, 148877)

from BoltzTraP2 import fermisurface

# with 2:3 we get the index of the vb, mu is in Ha
fermisurface.plot_fermisurface(data, bztI.equivalences, bztI.eband[2:3, :], mu=-0.003)

]]>

2019-03-11T00:00:00+00:002019-03-11T00:00:00+00:00/2019/03/11/Interface%20Reactions

Interface Reactions

This notebook shows how to

• Obtain information about interface reactions between two solid substances in contact, and
• Plot reaction energy as a function of mixing ratio.

We use the Materials Project API to obtain energies of compounds.

# Uncomment the subsequent lines in this cell to install dependencies for Google Colab.
# !pip install pymatgen==2022.7.19

from pymatgen.analysis.interface_reactions import InterfacialReactivity
from pymatgen.analysis.phase_diagram import GrandPotentialPhaseDiagram, PhaseDiagram
from pymatgen.core import Composition, Element
from pymatgen.ext.matproj import MPRester

%matplotlib inline

# Initialize the REST API interface. You may need to put your own API key in as an arg.
mpr = MPRester()

First, set the values of the two reactants. Optionally, simulate the case where the reaction system is in contact with an elemental reservoir.

Because the methodology here is to generate a pseudo-binary phase stability diagram of two reactants as a function of mixing ratio, the addition of an elemental reservoir implies construction of a so-called grand potential phase diagram.

# Chemical formulae for two solid reactants.
reactant1 = "LiCoO2"
reactant2 = "Li3PS4"

# Is the system open to an elemental reservoir?
grand = True

if grand:
    # Element in the elemental reservoir.
    open_el = "Co"
    # Relative chemical potential vs. pure substance. Must be non-positive.
    relative_mu = -1

Now, compile the critical reaction information:

# Get the compositions of the reactants
comp1 = Composition(reactant1)
comp2 = Composition(reactant2)

# Gather all elements involved in the chemical system.
elements = [e.symbol for e in comp1.elements + comp2.elements]
if grand:
    elements.append(open_el)
elements = list(set(elements))  # Remove duplicates

# Get all entries in the chemical system
entries = mpr.get_entries_in_chemsys(elements)

# Build a phase diagram using these entries.
pd = PhaseDiagram(entries)

# For an open system, include the grand potential phase diagram.
if grand:
    # Get the chemical potential of the pure subtance.
    mu = pd.get_transition_chempots(Element(open_el))[0]
    # Set the chemical potential in the elemental reservoir.
    chempots = {open_el: relative_mu + mu}
    # Build the grand potential phase diagram
    gpd = GrandPotentialPhaseDiagram(entries, chempots)
    # Create InterfacialReactivity object.
    interface = InterfacialReactivity(
        comp1,
        comp2,
        gpd,
        norm=True,
        include_no_mixing_energy=True,
        pd_non_grand=pd,
        use_hull_energy=False,
    )
else:
    interface = InterfacialReactivity(
        comp1,
        comp2,
        pd,
        norm=True,
        include_no_mixing_energy=False,
        pd_non_grand=None,
        use_hull_energy=False,
    )

From here, you can plot reaction energy versus mixing ratio as below. Note that the mixing ratio is between the normalized compositions of the reactants.

plt = interface.plot()

from collections import OrderedDict

import pandas as pd

critical_rxns = [
    OrderedDict(
        [
            ("Atomic fraction", round(ratio, 3)),
            ("Reaction equation", rxn),
            ("E$_{rxt}$ per mol equation (kJ/mol)", round(rxn_energy, 1)),
            ("E$_{rxt}$ per reactant atom (eV/atom)", round(reactivity, 3)),
        ]
    )
    for _, ratio, reactivity, rxn, rxn_energy in interface.get_kinks()
]
interface_reaction_table = pd.DataFrame(critical_rxns)
interface_reaction_table

	Atomic fraction	Reaction equation	E$_{rxt}$ per mol equation (kJ/mol)	E$_{rxt}$ per reactant atom (eV/atom)
0	0.000	Li3PS4 -> Li3PS4	0.0	0.000
1	0.500	0.3333 Li3PS4 + 0.6667 LiCoO2 -> 0.1667 Co + 0...	-206.4	-0.458
2	0.800	0.1111 Li3PS4 + 0.8889 LiCoO2 -> 0.8889 Co + 0...	-195.9	-0.571
3	0.842	0.08571 Li3PS4 + 0.9143 LiCoO2 -> 0.9143 Co + ...	-185.3	-0.560
4	0.850	0.08108 Li3PS4 + 0.9189 LiCoO2 -> 0.8649 Co + ...	-175.8	-0.535
5	1.000	LiCoO2 -> LiCoO2	-0.0	-0.000

You can also obtain the mixing ratio between the original compositions, i.e. mol fraction of the first reactant.

import numpy as np

interface_reaction_table["Molar fraction"] = pd.Series(
    np.round(interface.get_critical_original_kink_ratio(), 3)
)
interface_reaction_table

	Atomic fraction	Reaction equation	E$_{rxt}$ per mol equation (kJ/mol)	E$_{rxt}$ per reactant atom (eV/atom)	Molar fraction
0	0.000	Li3PS4 -> Li3PS4	0.0	0.000	0.000
1	0.500	0.3333 Li3PS4 + 0.6667 LiCoO2 -> 0.1667 Co + 0...	-206.4	-0.458	0.667
2	0.800	0.1111 Li3PS4 + 0.8889 LiCoO2 -> 0.8889 Co + 0...	-195.9	-0.571	0.889
3	0.842	0.08571 Li3PS4 + 0.9143 LiCoO2 -> 0.9143 Co + ...	-185.3	-0.560	0.914
4	0.850	0.08108 Li3PS4 + 0.9189 LiCoO2 -> 0.8649 Co + ...	-175.8	-0.535	0.919
5	1.000	LiCoO2 -> LiCoO2	-0.0	-0.000	1.000

Note that the reaction equations are Reaction objects suitable for structured analysis:

rxn = critical_rxns[2]["Reaction equation"]
print(rxn)
print(type(rxn))

0.1111 Li3PS4 + 0.8889 LiCoO2 -> 0.8889 Co + 0.3333 Li2SO4 + 0.1111 Li2S + 0.1111 Li3PO4
<class 'pymatgen.analysis.reaction_calculator.Reaction'>

# Get interface reaction information for reactants LiCoO2 and Li3PS4 in open system to Co.
kinks_from_API = mpr.get_interface_reactions(
    "LiCoO2", "Li3PS4", open_el="Co", relative_mu=-1, use_hull_energy=False
)

# Get inforamtion for the second critical reaction.
print(kinks_from_API[1])

{'ratio_atomic': 0.5000000000000001, 'ratio_molar': 0.6666666666666666, 'energy': -0.45837463535714296, 'rxn': Li3PS4 + 2 LiCoO2 -> 0.5 Co + Li2S + 1.5 CoS2 + Li3PO4, 'rxn_str': '0.333 Li3PS4 + 0.667 LiCoO2 -> 0.167 Co + 0.333 Li2S + 0.5 CoS2 + 0.333 Li3PO4', 'rxn_energy_sigdig': '-0.4584', 'rxn_energy_sigdig_kJmol': '-206.4', 'energy_per_mol_rxn_kJmol': -206.389932955848}

The critical reaction information from REST API should be the same as in the previous table:

critical_rxns_from_API = [
    OrderedDict(
        [
            ("Atomic fraction", round(reaction["ratio_atomic"], 3)),
            ("Molar fraction", round(reaction["ratio_molar"], 3)),
            ("Reaction equation", reaction["rxn_str"]),
            (
                "E$_{rxt}$ per mol equation (kJ/mol)",
                round(float(reaction["rxn_energy_sigdig_kJmol"]), 1),
            ),
            ("E$_{rxt}$ per reactant atom (eV/atom)", round(reaction["energy"], 3)),
        ]
    )
    for reaction in kinks_from_API
]
pd.DataFrame(critical_rxns_from_API)

	Atomic fraction	Molar fraction	Reaction equation	E$_{rxt}$ per mol equation (kJ/mol)	E$_{rxt}$ per reactant atom (eV/atom)
0	0.000	0.000	Li3PS4 -> Li3PS4	0.0	0.000
1	0.500	0.667	0.333 Li3PS4 + 0.667 LiCoO2 -> 0.167 Co + 0.33...	-206.4	-0.458
2	0.800	0.889	0.111 Li3PS4 + 0.889 LiCoO2 -> 0.889 Co + 0.33...	-195.9	-0.571
3	0.842	0.914	0.086 Li3PS4 + 0.914 LiCoO2 -> 0.914 Co + 0.11...	-185.3	-0.560
4	0.850	0.919	0.081 Li3PS4 + 0.919 LiCoO2 -> 0.865 Co + 0.05...	-175.8	-0.535
5	1.000	1.000	LiCoO2 -> LiCoO2	-0.0	-0.000

]]>

2019-01-11T00:00:00+00:002019-01-11T00:00:00+00:00/2019/01/11/How%20to%20plot%20and%20evaluate%20output%20files%20from%20Lobster

Introduction

This notebook was written by Janine George (E-mail: janine.george@bam.de BAM, https://jageo.github.io/).

This notebook shows how to plot Crystal Orbital Hamilton Population (COHP) and projected densities of states calculated with the Local-Orbital Basis Suite Towards Electronic-Structure Reconstruction (LOBSTER) code. Furtheremore, the classes Icohplist and Charge to evaluate ICOHPLIST.lobster and CHARGE.lobster are explained. See http://www.cohp.de for more information. The code to plot COHP and evaluate ICOHPLIST.lobster in pymatgen was started Marco Esters and extended by Janine George. The classes to plot DOSCAR.lobster, and to evaluate CHARGE.lobster were written by Janine George.

You can find our publication describing all modules and the implementation here: [1] J. George, G. Petretto, A. Naik, M. Esters, A. J. Jackson, R. Nelson, R. Dronskowski, G.-M. Rignanese, G. Hautier, ChemPlusChem n.d., n/a, e202200123.

# Uncomment the subsequent lines in this cell to install dependencies for Google Colab.
# !pip install pymatgen==2022.7.25

How to plot COHPCAR.lobster

get relevant classes

from pymatgen.electronic_structure.cohp import CompleteCohp
from pymatgen.electronic_structure.plotter import CohpPlotter

%matplotlib inline

get a completecohp object to simplify the plotting

COHPCAR_path = "lobster_data/GaAs/COHPCAR.lobster_new"
POSCAR_path = "lobster_data/GaAs/POSCAR_new"

completecohp = CompleteCohp.from_file(
    fmt="LOBSTER", filename=COHPCAR_path, structure_file=POSCAR_path
)

3809901

plot certain COHP

You have to search for the label of the COHP you would like to plot in ICOHPLIST.lobster

# search for the number of the COHP you would like to plot in ICOHPLIST.lobster (the numbers in COHPCAR.lobster are different!)
label = "16"
cp = CohpPlotter()
# get a nicer plot label
plotlabel = (
    str(completecohp.bonds[label]["sites"][0].species_string)
    + "-"
    + str(completecohp.bonds[label]["sites"][1].species_string)
)

cp.add_cohp(plotlabel, completecohp.get_cohp_by_label(label=label))
# check which COHP you are plotting

print(
    "This is a COHP between the following sites: "
    + str(completecohp.bonds[label]["sites"][0])
    + " and "
    + str(completecohp.bonds[label]["sites"][1])
)

x = cp.get_plot(integrated=False)
x.ylim([-10, 6])

x.show()

This is a COHP between the following sites: [0. 0. 0.] Ga and [0. 0. 0.] Ga

add several COHPs

# labels of the COHPs that will be summed!
labelist = ["16", "21"]
cp = CohpPlotter()
# get a nicer plot label
plotlabel = "two bonds"

cp.add_cohp(
    plotlabel,
    completecohp.get_summed_cohp_by_label_list(label_list=labelist, divisor=1),
)
x = cp.get_plot(integrated=False)
x.ylim([-10, 6])
x.show()

focus on certain orbitals only

# search for the number of the COHP you would like to plot in ICOHPLIST.lobster (the numbers in COHPCAR.lobster are different!)
label = "16"
cp = CohpPlotter()

# get orbital object
from pymatgen.electronic_structure.core import Orbital

# interaction between 4s and 4px
orbitals = [[4, Orbital.s], [4, Orbital.py]]
orbitals2 = [[4, Orbital.s], [4, Orbital.pz]]
# get a nicer plot label

plotlabel = (
    str(completecohp.bonds[label]["sites"][0].species_string)
    + "(4s)"
    + "-"
    + str(completecohp.bonds[label]["sites"][1].species_string)
    + "(4py)"
)
plotlabel2 = (
    str(completecohp.bonds[label]["sites"][0].species_string)
    + "(4s)"
    + "-"
    + str(completecohp.bonds[label]["sites"][1].species_string)
    + "(4pz)"
)

cp.add_cohp(
    plotlabel, completecohp.get_orbital_resolved_cohp(label=label, orbitals=orbitals)
)
cp.add_cohp(
    plotlabel2, completecohp.get_orbital_resolved_cohp(label=label, orbitals=orbitals2)
)
# check which COHP you are plotting

# with integrated=True, you can plot the integrated COHP
x = cp.get_plot(integrated=False)
x.ylim([-10, 6])

x.show()

How to evaluate ICOHPLIST.lobster

get relevant classes

from pymatgen.io.lobster import Icohplist

read in ICOHPLIST.lobster and get Icohpcollection object

icohplist = Icohplist(filename="lobster_data/GaAs/ICOHPLIST.lobster")
icohpcollection = icohplist.icohpcollection

get interesting properties from ICOHPLIST.lobster

# get icohp value by label (labelling according to ICOHPLIST.lobster)
# for spin polarized calculations you can also sum the spin channels
print("icohp value for certain bond by label")
label = "16"
print(icohpcollection.get_icohp_by_label(label))
print()
# you can get all Icohpvalue objects for certain bond lengths
print("Icohp values for certain bonds with certain bond lengths")
for key, icohp in icohpcollection.get_icohp_dict_by_bondlengths(
    minbondlength=0.0, maxbondlength=3.0
).items():
    print(key + ":" + str(icohp.icohp))
print()
# you can get all icohps for a certain site
print("ICOHP values of certain site")
for key, icohp in icohpcollection.get_icohp_dict_of_site(
    site=0, minbondlength=0.0, maxbondlength=3.0
).items():
    print(key + ":" + str(icohp.icohp))

icohp value for certain bond by label
-4.35464

Icohp values for certain bonds with certain bond lengths
16:{<Spin.up: 1>: -4.35464}
21:{<Spin.up: 1>: -4.35464}
23:{<Spin.up: 1>: -4.35464}
24:{<Spin.up: 1>: -4.35464}

ICOHP values of certain site
16:{<Spin.up: 1>: -4.35464}
21:{<Spin.up: 1>: -4.35464}
23:{<Spin.up: 1>: -4.35464}
24:{<Spin.up: 1>: -4.35464}

How to plot DOSCAR.lobster:

get relevant classes

from pymatgen.core.composition import Element
from pymatgen.electronic_structure.plotter import DosPlotter

# relevant classes
from pymatgen.io.lobster import Doscar

%matplotlib inline

read in DOSCAR.lobster and get structure object for later

# read in DOSCAR.lobster
doscar = Doscar(
    doscar="lobster_data/GaAs/DOSCAR.lobster", structure_file="lobster_data/GaAs/POSCAR"
)
complete_dos = doscar.completedos
# get structure object
structure = complete_dos.structure

plot total density of states

# plot total dos
Plotter = DosPlotter()
Plotter.add_dos("Total Dos", doscar.tdos)
Plotter.get_plot().show()

plot DOS projected on s, p, and d orbitals for certain element

# plot DOS of s,p, and d orbitals for certain element
Plotter = DosPlotter()
el = Element("Ga")
Plotter.add_dos_dict(complete_dos.get_element_spd_dos(el=el))
Plotter.get_plot().show()

plot DOS for cetain sites and orbitals

Plotter = DosPlotter()
# choose the sites you would like to plot
for isite, site in enumerate(structure[0:1]):
    # name the orbitals you would like to include
    # the other orbitals are named in a similar way. The orbitals are called: "s", "p_y", "p_z", "p_x", "d_xy", "d_yz", "d_z^2","d_xz", "d_x^2-y^2", "f_y(3x^2-y^2)", "f_xyz","f_yz^2", "f_z^3", "f_xz^2", "f_z(x^2-y^2)", "f_x(x^2-3y^2)"
    for orbital in ["4s"]:
        Plotter.add_dos(
            "Ga" + str(isite + 1) + ":" + orbital,
            complete_dos.get_site_orbital_dos(site, orbital),
        )
Plotter.get_plot().show()

evaluate CHARGE.lobster

get relevant classes

from pymatgen.io.lobster import Charge

read in charge and produce a structure with the charge as a property

charge = Charge(filename="lobster_data/GaAs/CHARGE.lobster")
newstructure = charge.get_structure_with_charges(
    structure_filename="lobster_data/GaAs/POSCAR"
)
print(newstructure)

Full Formula (Ga1 As1)
Reduced Formula: GaAs
abc   :   4.065993   4.065993   4.065993
angles:  60.000000  60.000000  60.000000
pbc   :       True       True       True
Sites (2)
  #  SP       a     b     c    Loewdin Charges    Mulliken Charges
---  ----  ----  ----  ----  -----------------  ------------------
  0  Ga    0     0     0                 -0.06                0.15
  1  As    0.75  0.75  0.75               0.06               -0.15

evaluate GROSSPOP.lobster

get relevant class

from pymatgen.io.lobster import Grosspop

grosspop = Grosspop(filename="lobster_data/GaAs/GROSSPOP.lobster")
print(grosspop.list_dict_grosspop)

[{'element': 'Ga', 'Mulliken GP': {'4s': 1.22, '4p_y': 0.54, '4p_z': 0.54, '4p_x': 0.54, '3d_xy': 2.0, '3d_yz': 2.0, '3d_z^2': 2.0, '3d_xz': 2.0, '3d_x^2-y^2': 2.0, 'total': 12.85}, 'Loewdin GP': {'4s': 1.1, '4p_y': 0.66, '4p_z': 0.66, '4p_x': 0.66, '3d_xy': 2.0, '3d_yz': 2.0, '3d_z^2': 2.0, '3d_xz': 2.0, '3d_x^2-y^2': 2.0, 'total': 13.06}}, {'element': 'As', 'Mulliken GP': {'4s': 1.64, '4p_y': 1.17, '4p_z': 1.17, '4p_x': 1.17, 'total': 5.15}, 'Loewdin GP': {'4s': 1.38, '4p_y': 1.19, '4p_z': 1.19, '4p_x': 1.19, 'total': 4.94}}]

get a structure with total gross populations

newstructure = grosspop.get_structure_with_total_grosspop("lobster_data/GaAs/POSCAR")
print("Structure:")
print(newstructure)

Structure:
Full Formula (Ga1 As1)
Reduced Formula: GaAs
abc   :   4.065993   4.065993   4.065993
angles:  60.000000  60.000000  60.000000
pbc   :       True       True       True
Sites (2)
  #  SP       a     b     c    Total Loewdin GP    Total Mulliken GP
---  ----  ----  ----  ----  ------------------  -------------------
  0  Ga    0     0     0                  13.06                12.85
  1  As    0.75  0.75  0.75                4.94                 5.15

FATBAND plot

get relevant classes

from pymatgen.electronic_structure.plotter import (
    BSDOSPlotter,
    BSPlotter,
    BSPlotterProjected,
)
from pymatgen.io.lobster import Fatband

get a bandstructure plot that is combined with a DOS plot

fatband = Fatband(
    filenames="lobster_data/GaAs",
    vasprun="lobster_data/GaAs/vasprun.xml",
    Kpointsfile="lobster_data/GaAs/KPOINTS",
)
# get a band structure object
bssymline = fatband.get_bandstructure()
# print(bssymline.as_dict())

# this can be plotted with the classes to plot bandstructures from vasp
BSDOSPlotter(bs_projection="elements", dos_projection="elements").get_plot(
    bs=bssymline, dos=complete_dos
).show()

/hpc-user/jgeorge/PycharmProjects/UpdateTutorials/pymatgen/pymatgen/io/lobster/outputs.py:1089: UserWarning: Make sure all relevant FATBAND files were generated and read in!
  warnings.warn("Make sure all relevant FATBAND files were generated and read in!")
/hpc-user/jgeorge/PycharmProjects/UpdateTutorials/pymatgen/pymatgen/io/lobster/outputs.py:1090: UserWarning: Use Lobster 3.2.0 or newer for fatband calculations!
  warnings.warn("Use Lobster 3.2.0 or newer for fatband calculations!")
findfont: Font family ['Times New Roman'] not found. Falling back to DejaVu Sans.
findfont: Font family ['Times New Roman'] not found. Falling back to DejaVu Sans.
findfont: Font family ['Times New Roman'] not found. Falling back to DejaVu Sans.

# another plot type from pymatgen:
bsplotter = BSPlotterProjected(bssymline)
bsplotter.get_projected_plots_dots(
    {"Ga": ["4s", "4p", "3d"], "As": ["4s", "4p"]}
).show()

Read lobsterout

from pymatgen.io.lobster import Lobsterout

get all relevant infos from lobsterout

lobsterout = Lobsterout("lobster_data/GaAs/lobsterout")

document = lobsterout.get_doc()

charge spilling can be accessed easily

print(document["chargespilling"])

[0.009899999999999999]

Create input files for vasp and lobster automatically

relevant classes

from pymatgen.io.lobster import Lobsterin

a Lobsterin object with standard settings is created, a standard basis is used

lobsterin = Lobsterin.standard_calculations_from_vasp_files(
    "lobster_data/GaAs/POSCAR",
    "lobster_data/GaAs/INCAR",
    "lobster_data/GaAs/POTCAR",
    option="standard",
)

/hpc-user/jgeorge/PycharmProjects/UpdateTutorials/pymatgen/pymatgen/io/lobster/inputs.py:670: UserWarning: Always check and test the provided basis functions. The spilling of your Lobster calculation might help
  warnings.warn(
/hpc-user/jgeorge/PycharmProjects/UpdateTutorials/pymatgen/pymatgen/io/vasp/inputs.py:1822: UnknownPotcarWarning: POTCAR with symbol Ga_d has metadata that does not match                          any VASP POTCAR known to pymatgen. The data in this                          POTCAR is known to match the following functionals:                          ['PBE_52', 'PBE_54', 'unvie_PBE_52']
  warnings.warn(
/hpc-user/jgeorge/PycharmProjects/UpdateTutorials/pymatgen/pymatgen/io/vasp/inputs.py:1822: UnknownPotcarWarning: POTCAR with symbol As has metadata that does not match                          any VASP POTCAR known to pymatgen. The data in this                          POTCAR is known to match the following functionals:                          ['PBE_52', 'PBE_54', 'unvie_PBE_52']
  warnings.warn(

writes lobsterin

lobsterin.write_lobsterin(path="lobsterin")
file = open("./lobsterin")
print(file.read())

COHPstartEnergy -35.0
COHPendEnergy 5.0
gaussianSmearingWidth 0.1
basisSet pbeVaspFit2015
cohpGenerator from 0.1 to 6.0 orbitalwise
saveProjectionToFile
basisfunctions Ga 3d 4p 4s 
basisfunctions As 4p 4s 

will change ISYM to -1, NSW to 0, insert NBANDS, and set LWAVE to True in the INCAR

additional changes to the INCAR are allowed via further_settings

lobsterin.write_INCAR(
    incar_input="lobster_data/GaAs/INCAR",
    incar_output="INCAR.lobster",
    poscar_input="lobster_data/GaAs/POSCAR",
    isym=-1,
    further_settings={"IBRION": -1},
)
file = open("./INCAR.lobster")
print(file.read())

ALGO = Normal
EDIFF = 1e-07
EDIFFG = 1e-05
ENCUT = 520
IBRION = -1
ISIF = 3
ISMEAR = 0
ISPIN = 1
ISYM = -1
LCHARG = False
LORBIT = 11
LWAVE = True
NBANDS = 13
NELM = 200
NELMIN = 4
NPAR = 4
NSW = 0
POTIM = 0.5
PREC = Accurate
SIGMA = 0.1

/hpc-user/jgeorge/PycharmProjects/UpdateTutorials/pymatgen/pymatgen/io/lobster/inputs.py:314: UserWarning: Please check your incar_input before using it. This method only changes three settings!
  warnings.warn("Please check your incar_input before using it. This method only changes three settings!")

a Lobsterin object with standard settings is created, a basis given by the user is used

lobsterin = Lobsterin.standard_calculations_from_vasp_files(
    "lobster_data/GaAs/POSCAR",
    "lobster_data/GaAs/INCAR",
    "lobster_data/GaAs/POTCAR",
    option="standard",
    dict_for_basis={"Ga": "4s 4p", "As": "4s 4p"},
)

# writes lobsterin
lobsterin.write_lobsterin(path="lobsterin")
file = open("./lobsterin")
print(file.read())

COHPstartEnergy -35.0
COHPendEnergy 5.0
gaussianSmearingWidth 0.1
basisSet pbeVaspFit2015
cohpGenerator from 0.1 to 6.0 orbitalwise
saveProjectionToFile
basisfunctions Ga 4s 4p
basisfunctions As 4s 4p

]]>

2019-01-04T00:00:00+00:002019-01-04T00:00:00+00:00/2019/01/04/How%20to%20use%20Boltztra2%20interface

How to use Boltztrap2 pymatgen interface

Author: Francesco Ricci, UCLouvain, francesco.ricci@uclouvain.be, frankyricci@gmail.com

Warning: under development. Please if you find any bug or issues contact me.

Here you find how to use boltztrap2 directly through pymatgen. Examples of what it is possible to do with it and which quantities can be computed from the band structure are provided.

Boltztrap2 needs to be installed. Spin polarized is now implemented.

This notebook was tested with pymatgen == 2v2020.7.3. It should work with higher version too.

%notebook inline

from pymatgen.electronic_structure.boltztrap2 import *
from monty.serialization import loadfn

Load DFT data

Deprecated Loaders

Please, do not use them anymore. They will be removed any time soon.

data = VasprunLoader().from_file('boltztrap2_data/vasprun-PbTe_uniform_bs.xml')

/home/tardo/github/matgenb/notebooks/boltztrap2.py:347: UserWarning: Deprecated Loader. Use VasprunBSLoader instead.
  warnings.warn("Deprecated Loader. Use VasprunBSLoader instead.")
/usr/lib64/python3.7/site-packages/pymatgen/io/vasp/outputs.py:886: UserWarning: No POTCAR file with matching TITEL fields was found in /home/tardo/github/matgenb/notebooks/boltztrap2_data
  " was found in {}".format(os.path.abspath(p)))

bs = loadfn('boltztrap2_data/PbTe_bandstructure.json')
loader = BandstructureLoader(bs,data.structure)

/home/tardo/github/matgenb/notebooks/boltztrap2.py:220: UserWarning: Deprecated Loader. Use VasprunBSLoader instead.
  warnings.warn("Deprecated Loader. Use VasprunBSLoader instead.")

New unique loader

You can load both vasprun or band structure pmg obj or directly from a vasprun.xml file.

vrun = Vasprun('boltztrap2_data/vasprun-PbTe_uniform_bs.xml',parse_projected_eigen=True)
data = VasprunBSLoader(vrun)

bs = vrun.get_band_structure()
nele = vrun.parameters['NELECT']
st = vrun.final_structure
data = VasprunBSLoader(bs,structure=st,nelect=nele)

data = VasprunBSLoader.from_file('boltztrap2_data/vasprun-PbTe_uniform_bs.xml')

Interpolator

# set curvature=False to speed up in case you do not need effective mass or hall coeficients
bztInterp = BztInterpolator(data,lpfac=10,energy_range=1.5,curvature=True)

Compute and plot the band structure on standard symmetry path

sbs = bztInterp.get_band_structure()
list(sbs.bands.values())[0].shape

(6, 137)

from pymatgen.electronic_structure.plotter import BSPlotter

BSPlotter(sbs).show()

On a specific kpoint path

# define the path as a list
kpaths = [['L','K']]
# set the fractional coordinates of the kpoint as a dict
kp_lbl = {'L':np.array([0.5,0.5,0.5]),'K': np.array([0.375, 0.375, 0.75 ])}
sbs = bztInterp.get_band_structure(kpaths,kp_lbl)
BSPlotter(sbs).show()