The MaterialsWeb API is built on the Materials API (MAPI). The best way to access our database is through the MWRester class implemented in QuantumML as shown below.

Connecting to materialsweb

from quantumML.rest import MWRester
with MWRester() as mwr:
    mwr.get_calculation(filter/s)

Filters

To refine the query results the following filters may be applied

band_gap_range
Range of bandgaps in eV as a two valued list eg. [1.2, 3.1]
Formation_energy_range
Range of formation energies in meV as two values list eg. [50, 150]
elements
List of elements contained in entry eg. ['Na', 'Cl']
note: this will return all entries that contain Na and Cl as well as any other element
space_group_number
integer of space group number eg. 5
dimension
integer of dimension of material eg. 2
crystal_system
crystal system as a string eg. 'Tetragonal'

The below code shows how to apply the filters for a querry:

Applying filters

from quantumML.rest importb> MWRester
with MWRester() as mwr:
    mwr.get_calculation(band_gap_range=[1,1.5], formation_energy_range=[124,150])

This will store the querry as a list of Json response that can be accessed by mwr.results. A single Json response is shown below:

Json response

print(mwr.results[1])

Out:
{'url': 'http://172.16.170.227:7800/rest/calculation/106/',
 'configuration': None,
 'dimension': 2,
 'label': 'mp-700',
 'path': '/var/www/materialsweb/static/database/mp-700',
 'natoms': 4,
 'settings': "{'potentials': [{'name': 'Ge_d', 'xc': 'PBE', 'us': False, 'paw': True}, {'name': 'Se', 'xc': 'PBE', 'us': False, 'paw': True}], 'nbands': 28, 'prec': 'accura', 'istart': 0, 'icharg': 2, 'ispin': 2, 'encut': 500.0, 'nelm': 60, 'nelmin': 2, 'ediff': 1e-06, 'lreal': True, 'nsw': 50, 'ibrion': 2, 'isif': 3, 'potim': 0.5, 'pstress': 0.0, 'ismear': 1, 'sigma': 0.1, 'algo': 'fast', 'lwave': True, 'lcharg': True, 'lvtot': False, 'lorbit': 0, 'ldipol': False, 'idipol': 0, 'epsilon': 1.0}",
 'energy': -10.4702046,
 'energy_pa': -2.61755115,
 'magmom': -0.0001349,
 'magmom_pa': -3.3725e-05,
 'band_gap': 1.2107,
 'is_direct': False,
 'irreducible_kpoints': 42.0,
 'formation_energy': 133.0,
 'attempt': 0,
 'nsteps': 32,
 'converged': True,
 'runtime': None,
 'entry': 'http://172.16.170.227:7800/rest/entry/113/',
 'composition': 'http://172.16.170.227:7800/rest/composition/Ge1%20Se1/',
 'input': 'http://172.16.170.227:7800/rest/structure/325/',
 'output': 'http://172.16.170.227:7800/rest/structure/324/',
 'dos': 'http://172.16.170.227:7800/rest/dos/107/',
 'element_set': ['http://172.16.170.227:7800/rest/elements/Ge/',
  'http://172.16.170.227:7800/rest/elements/Se/']}

The content of each querry element can be accessed through pythons dictionary syntax as shown below:

Printing results

for results in mwr.results:
    print(results['composition'] + '\t Bandgap = ' + str(results['band_gap'])
Out:
Pd120S2			 BandGap = 1.1746
Ge120Se1		 BandGap = 1.2107
Pd120Se2		 BandGap = 1.3964
I220Pt1			 BandGap = 1.4215
I220Pd1			 BandGap = 1.1512
Br320Cr1		 BandGap = 1.4714
Cr120H120O2		 BandGap = 1.0072
Mo120O3			 BandGap = 1.1381

Write files

currently there are two methods to write the files from a query. The get_calculation method will store all the entries as a list. As a result one can write all of the files from a querry to a directory using the write_all method or write the files of a specific entry using the write method:

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