Example: Bulk

In this example the global optimization of a Si4 crystal is performed using the openKim ASE calculator. To use this calculator the KIM API package as well as kimpy is required. The codes are hosted in GitHub and is explained on official openKim Webpage. Please follow the installation instructions on these website to install the calculator properly. For this example any silicon force field can be used, however, in the python script by default the Sillinger-Weber (SW_StillingerWeber_1985_Si__MO_405512056662_005) potential is used. After having installed the KIM API properly the potential can be installed via the following command: .. code-block:

kim-api-collections-management install user SW_StillingerWeber_1985_Si__MO_405512056662_005

Exercice 1: Distinguishing minima

The aim of this tutorial is to determine the fingerprint distance of structures which can be considered to be the same and structures which are different. The idea is to perform various MD trajectories and relax them back to the same local minimum with a certain force norm. To do this task the package includes a class adjust_fp which is doing this task automatically. First we need all the imports which are required:

from minimahopping.adjust_fp import adjust_fp
import logging
from ase.lattice.cubic import FaceCenteredCubic
from ase.calculators.kim.kim import KIM

Now we create a structure and set up a calculator. In this example we create a silicon fcc structure and we set up the openKim calculator:

logging.INFO
atoms = FaceCenteredCubic(symbol='Si', latticeconstant=4., size=(1,1,1))
calculator = KIM("SW_StillingerWeber_1985_Si__MO_405512056662_005")
atoms.calc = calculator

Afterwards we set up the adjustment class.

fnrm =  0.001
adjust = adjust_fp(initial_configuration=atoms,
                   iterations=10,
                   T0=100,
                   dt0=0.01,
                   mdmin=1,
                   n_S_orbitals=1,
                   n_P_orbitals=1,
                   width_cutoff=4,
                   fmax=fnrm,
                   write_graph_output=False)

Now we run 100 mds followed by a geometry optimization.

outdict = adjust.run()

A dictionairy containing all the information is returned and can be printed the following way:

msg = "\n=======================FINGERPRINT================================\n"
msg += '\nMaximal fingerprint distance between the same local minima:\n' + str(fp_max)
msg += '\n Mean fingerprint distance between the same local minima:\n' + str(fp_mean)
msg += '\n Standard deviaton of the fingerprint distances:\n' + str(fp_std)
msg += '\n Suggested minimum threshold (mean + 3 * std):\n' + str(fp_mean + 3 * fp_std)
msg += "\n==================================================================\n"
print(msg)

e_max = outdict['energy']['max']
e_mean = outdict['energy']['mean']
e_std = outdict['energy']['std']
msg = "\n=========================ENERGIES=================================\n"
msg += '\nMaximal difference between the same local minima:\n' + str(e_max)
msg += '\n Mean energy difference between the same local minima:\n' + str(e_mean)
msg += '\n Standard deviaton of the energy differences:\n' + str(e_std)
msg += '\n Suggested energy threshold (mean + 3 * std):\n' + str(e_mean + 3 * e_std)
msg += "\n==================================================================\n"
print(msg)

Note

Be aware that it is very important to use the same parameters for the calculation of the energy and force, the OMFP and the local geometry optimization in the Minima Hopping method.

Exercise 2: Starting Minimahopping

The aim of this tutorial is to start the minima hopping algorithm with the given default settings. If you want to use different parameters you can find a detailed description of them here First all the required classes are imported:

from ase.lattice.cubic import FaceCenteredCubic
from ase.calculators.kim.kim import KIM
from minimahopping.minhop import Minimahopping

Now we create a structure and set up a calculator. As in exercise 1 we create the structure of a 4 atoms Silicon crystal and we set up the corresponding calculator:

initial_configuration = FaceCenteredCubic(symbol='Si', latticeconstant=5.25, size=(1,1,1))

In a next step we set up the openKim calculator

calculator = KIM("SW_StillingerWeber_1985_Si__MO_405512056662_005")
initial_configuration.calc = calculator

Now we can set up the minima hopping class and run it:

with Minimahopping(initial_configuration,
                   verbose_output=True,
                   T0=2000,
                   dt0=0.1,
                   use_MPI=False) as mh:
    mh(totalsteps=50)

The minima hopping algorithm cycles now through 50 escape loops.

Note

If a second calculator is desired this can easily be done by setting up a second md calculator and give it as an argument to the `MinimaHopping` class.

calculator = SOME_ASE_CALCULATOR
md_calculator = SOME_OTHER_ASE_CALCULATOR

with Minimahopping(initial_configuration,
                   md_calculator = md_calculator
                   verbose_output=True,
                   T0=2000,
                   dt0=0.1,
                   use_MPI=False) as mh:

mh(totalsteps=50)

Caution

Be aware that in case you want to examine periodic systems your calculator needs the stress property included so that variable cell shape md and geometry optimization is possible.