# 4. Screening¶

For the screening part of the work, you can choose one of two possible routes:

1. Quick and simple: Use for loops in your scripts to loop over the structures in your database, submitting in total 1 (Zeo++) + 2 (Raspa) = 3 calculations per structure.

This should require very little changes to your python scripts and is a perfectly valid solution.

2. Reusable and elegant: Write an AiiDA Workchain that takes a structure, performs all necessary calculations, and outputs the result.

This route requires more advanced python concepts and involves a bit of coding, but makes your workflow more robust and reusable.

## 4.1. Quick and simple¶

Just use the QueryBuilder to load the CifData nodes from the AiiDA database and loop over them.

Computed properties are automatically linked to CifData nodes via calculation nodes.

Try verdi graph generate <PK> on a zeo++ or RASPA calculation node to get an overview of the AiiDA graph.

In order to automatically determine how many unit cells to use in the simulation, you may use the following function for convenience:

def multiply_cell(cif, cutoff):
""" Determine number of replica of unit cell.

Works for cells of arbitrary shape.

:param cif:  CifData object
:param cutoff:  cutoff radius of interaction
:returns:  String of integers specifying replica of unit cell,
suitable for 'UnitCells' parameter of raspa calculation.
"""
from math import cos, sin, sqrt, pi
import numpy as np

struct = cif.values.dictionary.itervalues().next()

a = float(struct['_cell_length_a'])
b = float(struct['_cell_length_b'])
c = float(struct['_cell_length_c'])

# compute cell vectors following https://en.wikipedia.org/wiki/Fractional_coordinates
v = sqrt(1-cos(alpha)**2-cos(beta)**2- cos(gamma)**2+2*cos(alpha)*cos(beta)*cos(gamma))
cell=np.zeros((3,3))
cell[0,:] = [a, 0, 0]
cell[1,:] = [b*cos(gamma), b*sin(gamma),0]
cell[2,:] = [c*cos(beta), c*(cos(alpha)-cos(beta)*cos(gamma))/(sin(gamma)),
c*v /sin(gamma)]
cell=np.array(cell)

# diagonalize the cell matrix
diag = np.diag(cell)
# and computing nx, ny and nz
nx, ny, nz = tuple(int(i) for i in np.ceil(cutoff/diag*2.))

#return nx, ny, nz
return "{} {} {}".format(nx, ny, nz)


## 4.2. Elegant and robust¶

Combine the calculations into a workchain using the AiiDA WorkChain class. Here one should define the list of input types using spec.input() function and the workflow steps using spec.outline() function. In our case the workflow takes as input CifData object with structure and the names of Zeo++ and Raspa codes.

class DcMethane(WorkChain):
""" Compute deliverable capacity for methane. """

@classmethod
def define(cls, spec):
""" Define input, logic and output of Workchain. """
super(DcMethane, cls).define(spec)

# First we define the inputs, specifying the type we expect
spec.input("structure", valid_type=CifData, required=True)
spec.input("zeopp_codename", valid_type=Str, required=True)
spec.input("raspa_codename", valid_type=Str, required=True)

# The outline describes the business logic that defines
# which steps are executed in what order and based on
# what conditions. We will implement each cls.method below
spec.outline(
cls.init,
cls.run_geom_zeopp,
cls.extract_results,
)

# Here we define the output the Workchain will generate and
# return. Dynamic output allows a variety of AiiDA data nodes
# to be returned
spec.dynamic_output()


The workchain consists of 7 steps.

### 4.2.1. Step 1: Prepare input parameters and variables¶

def init(self):
"""
Initialize variables
"""
# Define cutoff for the methane-methane interactions
cutoff = 12.00

self.ctx.parameters = {<adapt the parameters dictionary defined in the section 3>}
# Note: You'll need the multiply_cell function mentioned in section 4.1

self.ctx.options = {
"resources": {
"num_machines": 1,
"tot_num_mpiprocs": 1,
"num_mpiprocs_per_machine": 1,
},
"max_wallclock_seconds": 10 * 60 * 60, # 10 hours
"max_memory_kb": 2000000, # limiting the
"withmpi": False,
}


### 4.2.2. Step 2: Compute the geometric parameters of the MOFs¶

Draw upon how we submitted Zeo++ calculations in section 2. The main difference here is that the calculation inputs, such as Code or structure, are provided as a dictionary.

def run_geom_zeopp(self):
""" Perform a zeo++ calculation. """

# Create the input dictionary
NetworkParameters = DataFactory('zeopp.parameters')
sigma = 1.86
params = {
'ha': True,
'res': True,
'sa': [sigma, sigma, 100000],
'volpo': [sigma, sigma, 100000],
}

inputs = {
'code':        : Code.get_from_string(self.inputs.zeopp_codename.value),
'structure':   : self.inputs.structure,
'parameters':  : NetworkParameters(dict=params),
'_options':    : self.ctx.options,
}

# Create the calculation process and launch it
process = ZeoppCalculation.process()
future  = submit(process, **inputs)
self.report("pk: {} | Running geometry analysis with zeo++".format(future.pid))



Steps 3 and 5 compute the methane loading in units of [molecules/cell] at 5.8 and 65 bars respectively. Since the same calculation is performed twice in this workchain, we put the common part of those steps into a function:

def _run_loading_raspa(self, pressure):
""" Perform a raspa calculation for one pressure. """
self.ctx.parameters['GeneralSettings']['ExternalPressure'] = pressure

# Create the input dictionary
inputs = {
'code'       : Code.get_from_string(self.inputs.zeopp_codename.value),
'structure'  : self.inputs.structure,
'parameters' : NetworkParameters(dict=params),
'_options'   : self.ctx.options,
}

# Create the calculation process and launch it
process = RaspaCalculation.process()
future = submit(process, **inputs)
self.report("pk: {} | Running raspa for the pressure {} [bar]" \
.format(future.pid, pressure/1e5)



The run_loading_raspa_5_8 and run_loading_raspa_65 functions are defined as follows:

def run_loading_raspa_5_8(self):



Steps 4 and 6 extract pressure and methane loading from the input and output parameters of the calculation and put them into context (ctx) that is used to store any data that should be persisted between step.

def parse_loading_raspa(self):
""" Extract the pressure and loading average of the last completed raspa calculation """
pressure = self.ctx.parameters['GeneralSettings']['ExternalPressure']


Last step stores the selected computed parameters as the output of the DcMethane workchain:

def extract_results(self):
""" Attach the results of raspa calculation and the initial structure to the outputs """

zeopp = self.ctx.zeopp
res = {
'density'                        : zeopp['pore_volume_volpo'].get_attr('Density'),
'density_units'                  : 'g/cm^3',
'pore_accesible_volume'          : zeopp['pore_volume_volpo'].get_attr('POAV_A^3'),
'pore_accesible_volume_units'    : 'A^3',
'unitcell_volume'                : zeopp['pore_volume_volpo'].get_attr( 'Unitcell_volume'),
'unitcell_volume_units'          : 'A^3',
'largest_included_sphere'        : zeopp['free_sphere_res'].get_attr('Largest_included_sphere'),
'largest_included_sphere_units'  : 'A',
'accessible_surface_area'        : zeopp['surface_area_sa'].get_attr('ASA_m^2/g'),
'accessible_surface_area_units'  : 'm^2/g',
'deliverable_capacity'           : dc,
'deliverable_capacity_units'     : 'molecules/unit cell',
}
self.out("result",  ParameterData(dict=res))

self.report("Workchain <{}> completed successfully".format(self.calc.pk))
return


To submit the calculation please adapt the following script. Please note, the file containing the DcMethane workchain should be accessible from the python shell. To achieve that just place the file into a folder listed in PYTHONPATH system variable and rename it to deliverable_capacity.py.

import os
from deliverable_capacity import DcMethane
from aiida.orm.data.cif import CifData
from aiida.orm.data.base import Str
from aiida.work.run import run, submit

for s in structures:
outputs = submit(DcMethane, structure=s,
zeopp_codename=Str('zeopp@deneb-molsim'), raspa_codename=Str('raspa@deneb-molsim'),
)


Where structures is the list of CifData nodes stored in your AiiDA database.