The Kitchin Research Group

In a previous post I discussed how to represent a single DFT calculation in a JSON file that could easily be shared, and reanalyzed. Here I look at sharing a series of calculations. I had previously run calculations to analyze an equation of state for Cu. Here we create a list of the JSON representations of each calculation, and save it in one overall JSON file. We will give the data some structure and documentation. JSON represents dictionaries very well, so we build a dictionary of the results and then "dump" that dictionary to a JSON file.

from jasp import *

LC = [3.5, 3.55, 3.6, 3.65, 3.7, 3.75]

data = {'doc':'JSON file containing a set of calculations for the equation of state of Cu'}

data['calculations'] = []

for a in LC:
    with jasp('../bulk/Cu-{0}'.format(a)) as calc:
        data['calculations'].append(calc.dict)

import json
with open('eos.json', 'wb') as f:
    f.write(json.dumps(data))

Now, you can view this eos.json file, and analyze it yourself as follows. In python we read the file in and convert it to a data structure using the json module.

import json

with open('eos.json', 'rb') as f:
    d = json.loads(f.read())

volumes =  [entry['data']['volume'] for entry in d['calculations']]
energies = [entry['data']['total_energy'] for entry in d['calculations']]

import matplotlib.pyplot as plt
plt.plot(volumes, energies)
plt.xlabel('Volume ($\AA$)')
plt.ylabel('Total energy (eV)')
plt.savefig('eos-from-json.png')
plt.show()

If you wanted to do further analysis you could. Suppose you wanted to know more detail about how that calculation was done? You can retrieve the INCAR, KPOINT, and POTCAR details for each calculation. Any parameter not listed here was not specified in the calculations.

import json

with open('eos.json', 'rb') as f:
    d = json.loads(f.read())

# print details for the first calculation in the list
print d['calculations'][0]['incar']
print d['calculations'][0]['input']
print d['calculations'][0]['potcar']

{u'doc': u'INCAR parameters', u'nbands': 9, u'encut': 350.0, u'prec': u'Normal'}
{u'kpts': [8, 8, 8], u'reciprocal': False, u'xc': u'PBE', u'kpts_nintersections': None, u'setups': None, u'txt': u'-', u'gamma': False}
[[u'Cu', u'potpaw_PBE/Cu/POTCAR', u'a44c591415026f53deb16a99ca3f06b1e69be10b']]

The POTCAR information contains the species, the path to the POTCAR, and a git-hash of the POTCAR file. That way you can tell if you used exactly the same file that I did. You can compute a git hash like this :

cat /home-research/jkitchin/src/vasp/potpaw_PBE/Cu/POTCAR | git hash-object --stdin

a44c591415026f53deb16a99ca3f06b1e69be10b

If you want to get the details of the geometry of some calculation, you do it this way:

import json

with open('eos.json', 'rb') as f:
    d = json.loads(f.read())

print d['calculations'][0]['atoms'].keys()
print 'cell = ', d['calculations'][0]['atoms']['cell']
print 'syms = ', d['calculations'][0]['atoms']['symbols']
print 'cpos = ', d['calculations'][0]['atoms']['positions']

[u'cell', u'symbols', u'positions', u'pbc', u'tags']
cell =  [[1.75, 1.75, 0.0], [0.0, 1.75, 1.75], [1.75, 0.0, 1.75]]
syms =  [u'Cu']
cpos =  [[0.0, 0.0, 0.0]]

You can do further analysis from there.

import json

with open('eos.json', 'rb') as f:
    d = json.loads(f.read())

print d['doc']
print
print d['calculations'][0]['doc']

JSON file containing a set of calculations for the equation of state of Cu

JSON representation of a VASP calculation.

energy is in eV
forces are in eV/\AA
stress is in GPa (sxx, syy, szz,  syz, sxz, sxy)
magnetic moments are in Bohr-magneton
The density of states is reported with E_f at 0 eV.
Volume is reported in \AA^3
Coordinates and cell parameters are reported in \AA

If atom-projected dos are included they are in the form:
{ados:{energy:data, {atom index: {orbital : dos}}}

Copyright (C) 2013 by John Kitchin. See the License for information about copying.

org-mode source