79 lines
3.1 KiB
Python
79 lines
3.1 KiB
Python
# coding: utf-8
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# import all we need and start by msspec
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from msspec.calculator import MSSPEC
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# we will build a simple atomic chain
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from ase import Atom, Atoms
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# we need some numpy functions
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import numpy as np
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symbol = 'Ni' # The kind of atom for the chain
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orders = (1, 5) # We will run the calculation for single scattering
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# and multiple scattering (5th diffusion order)
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chain_lengths = (2,3,5) # We will run the calculation for differnt lengths
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# of the atomic chain
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a = 3.499 * np.sqrt(2)/2 # The distance bewteen 2 atoms
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# Define an empty variable to store all the results
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all_data = None
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# 2 for nested loops over the chain length and the order of diffusion
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for chain_length in chain_lengths:
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for order in orders:
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# We build the atomic chain by
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# 1- stacking each atom one by one along the z axis
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chain = Atoms([Atom(symbol, position = (0., 0., i*a)) for i in
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range(chain_length)])
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# 2- rotating the chain by 45 degrees with respect to the y axis
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#chain.rotate('y', np.radians(45.))
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chain.rotate(45., 'y')
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# 3- setting a custom Muffin-tin radius of 1.5 angstroms for all
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# atoms (needed if you want to enlarge the distance between
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# the atoms while keeping the radius constant)
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#[atom.set('mt_radius', 1.5) for atom in chain]
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# 4- defining the absorber to be the first atom in the chain at
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# x = y = z = 0
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chain.absorber = 0
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# We define a new PED calculator
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calc = MSSPEC(spectroscopy = 'PED')
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calc.set_atoms(chain)
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# Here is how to tweak the scattering order
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calc.calculation_parameters.scattering_order = order
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# This line below is where we actually run the calculation
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all_data = calc.get_theta_scan(level='3s', #kinetic_energy=1000.,
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theta=np.arange(0., 80.), data=all_data)
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# OPTIONAL, to improve the display of the data we will change the dataset
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# default title as well as the plot title
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t = "order {:d}, n = {:d}".format(order, chain_length) # A useful title
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dset = all_data[-1] # get the last dataset
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dset.title = t # change its title
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# get its last view (there is only one defined for each dataset)
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v = dset.views()[-1]
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v.set_plot_options(title=t) # change the title of the figure
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# OPTIONAL, set the same scale for all plots
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# 1. iterate over all computed cross_sections to find the absolute minimum and
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# maximum of the data
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min_cs = max_cs = 0
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for dset in all_data:
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min_cs = min(min_cs, np.min(dset.cross_section))
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max_cs = max(max_cs, np.max(dset.cross_section))
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# 2. for each view in each dataset, change the scale accordingly
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for dset in all_data:
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v = dset.views()[-1]
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v.set_plot_options(ylim=[min_cs, max_cs])
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# Pop up the graphical window
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all_data.view()
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# You can end your script with the line below to remove the temporary
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# folder needed for the calculation
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calc.shutdown()
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