mammos_spindynamics quickstart#

mammos_spindynamics provides temperature-dependent spontaneous magnetization values.

  • db contains pre-computed materials

[1]:

import mammos_spindynamics

Querying the database – mammos_spindynamics.db#

Use the following function to get a list of all available materials:

[2]:

mammos_spindynamics.db.find_materials()

[2]:
chemical_formula space_group_name space_group_number cell_length_a cell_length_b cell_length_c cell_angle_alpha cell_angle_beta cell_angle_gamma cell_volume ICSD_label OQMD_label label source
0 Co2Fe2H4 P6_3/mmc 194 2.645345 Angstrom 2.645314 Angstrom 8.539476 Angstrom 90.0 deg 90.0 deg 120.0 deg 51.751119 Angstrom3 0001 Uppsala
1 Y2Ti4Fe18 P4/mbm 127 8.186244 Angstrom 8.186244 Angstrom 4.892896 Angstrom 90.0 deg 90.0 deg 90.0 deg 327.8954234 Angstrom3 0002 Uppsala
2 Nd2Fe14B P42/mnm 136 8.78 Angstrom 8.78 Angstrom 12.12 Angstrom 90.0 deg 90.0 deg 90.0 deg 933.42 Angstrom3 0003 https://doi.org/10.1103/PhysRevB.99.214409
3 Fe16N2 ? 0 nan Angstrom nan Angstrom nan Angstrom nan deg nan deg nan deg nan Angstrom3 0005 Uppsala
4 Ni80Fe20 Pm-3m 221 3.55 Angstrom 3.55 Angstrom 3.55 Angstrom 90.0 deg 90.0 deg 90.0 deg 44.738875 Angstrom3 0006 https://doi.org/10.48550/arXiv.1908.08885; spa...

Use the following function to get an object that contains spontaneous magnetization Ms at temperatures T:

[3]:

results_spindynamics = mammos_spindynamics.db.get_spontaneous_magnetization("Co2Fe2H4")

The result object provides a function to plot the data for visual inspection:

[4]:

results_spindynamics.plot()

[4]:

<Axes: xlabel='Thermodynamic Temperature (K)', ylabel='Spontaneous Magnetization (A / m)'>
../../_images/examples_mammos-spindynamics_quickstart_7_1.png

We can access the Ms and T attributes and get mammos_entity.Entity objects:

[5]:

results_spindynamics.Ms

[5]:

SpontaneousMagnetization(value=
[1160762.15152728 1152764.540031   1144332.38021977 1136095.4237953
 1128134.37709324 1119860.36127139 1111776.43793683 1102918.50364088
 1094392.01798628 1085670.06372291 1076778.81897829 1067425.28513189
 1057970.97409635 1048538.21055569 1039913.8221449  1029517.07701433
 1019718.19960104 1010212.40682623 1000827.01766803  990071.55620798
  977847.7971226   967365.03404838  957943.74673504  945368.08824221
  933306.33678552  924042.80638082  911156.5370935   898923.20851189
  886959.86875088  874610.18656417  859777.5032481   846674.7339648
  833341.85234593  817215.36597138  802915.55664802  788770.83767705
  767322.55015624  749504.37038855  733715.25355713  716555.57268794
  695546.66554008  675931.69422208  650394.96667178  623964.15571257
  595267.15471822  574337.71381396  540677.01870906  506480.06838947
  465749.37929254  419581.94398809  363556.9147052   283780.87532889
  116330.7659029    45920.08375439   28204.38382936   22252.02685696
   18336.06448858   16018.35637159   14232.77360447   13649.49689592
   12290.85695223   11386.3050879    10781.61435564   10306.54019184
    9665.93883184    9395.09234065    8975.25861354    8438.06343775
    8343.59372938    8102.70528087    7747.89846694    7593.11190125
    7321.35684554    7307.02274373    7047.92251168    7129.16483945
    6674.61311621    6599.17726486    6616.13202518   12484.21016472],
 unit=A / m)

[6]:

results_spindynamics.T

[6]:

ThermodynamicTemperature(value=
[  20.   40.   60.   80.  100.  120.  140.  160.  180.  200.  220.  240.
  260.  280.  300.  320.  340.  360.  380.  400.  420.  440.  460.  480.
  500.  520.  540.  560.  580.  600.  620.  640.  660.  680.  700.  720.
  740.  760.  780.  800.  820.  840.  860.  880.  900.  920.  940.  960.
  980. 1000. 1020. 1040. 1060. 1080. 1100. 1120. 1140. 1160. 1180. 1200.
 1220. 1240. 1260. 1280. 1300. 1320. 1340. 1360. 1380. 1400. 1420. 1440.
 1460. 1480. 1500. 1520. 1540. 1560. 1580. 1600.],
 unit=K)

To work with the data we can also get it as pandas.DataFrame. The dataframe only contains the values (not units).

[7]:

results_spindynamics.dataframe

[7]:
T Ms
0 20.0 1.160762e+06
1 40.0 1.152765e+06
2 60.0 1.144332e+06
3 80.0 1.136095e+06
4 100.0 1.128134e+06
... ... ...
75 1520.0 7.129165e+03
76 1540.0 6.674613e+03
77 1560.0 6.599177e+03
78 1580.0 6.616132e+03
79 1600.0 1.248421e+04

80 rows × 2 columns

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