Implementation of Gamma Surface calculation

The Gamma Surface calculation requires multiple calculations, therefore we use the ParallelMaster Class and implement a ParallelMaster for Gamma Surface calculations.

In [1]:
import numpy as np
import matplotlib.pylab as plt
In [2]:
from pyiron import Project

Class templates

We import two additional classes the AtomisticParallelMaster and the JobGenerator

In [3]:
from pyiron.atomistics.master.parallel import AtomisticParallelMaster
from pyiron_base.master.parallel import JobGenerator


The JobGenerator has three primary functions:

  • parameter_list() which generates a list of parameters, each parameter can then be executed in parallel.
  • job_name() a function to rename the temlate job using one parameter out of the parameter list.
  • modify_job() the function which modifies the job based on one parameter out of the parameter list. Finally there is one additional function to construct the structures the get_structure() function.
In [4]:
class GammaJobGenerator(JobGenerator):
    def parameter_list(self):

        parameter_lst = []
        structure = self._job.ref_job.structure
        x_max = structure.cell[0, 0]
        y_max = structure.cell[1, 1]
        x_vec = np.linspace(0, x_max, self._job.input["n_mesh_x"])
        y_vec = np.linspace(0, y_max, self._job.input["n_mesh_y"])
        for x in x_vec:
            for y in y_vec:
                parameter_lst.append([structure.copy(), x,y])
        return parameter_lst

    def job_name(parameter):
        return 'x_{:.4}_y_{:.4}'.format(parameter[1], parameter[2]).replace('.', '_')

    def modify_job(self, job, parameter):
        job.structure = self.get_structure(structure=parameter[0], x=parameter[1], y=parameter[2])
        return job
    def get_structure(structure, x, y):
        z = structure.positions[:, 2]
        z_0 = np.mean(z)
        structure.positions[z < z_0, 0] += x
        structure.positions[z < z_0, 1] += y
        structure.add_tag(selective_dynamics=[False, False, True])
        structure.pbc[2] = True
        return structure


The ParallelMaster includes the JobGenerator as an object and in addition adds auxiliary functions to simplify the interaction of the user with the class. In this case these are the collect_output() function which summarizes the results of the individual collection as well as two plot functions the regular plot() function and the plot2d() function to visualise the results. In general the ParallelMaster primarly implements the functionality to aggregate the data once the calculation is finished.

In [5]:
class GammaSurface(AtomisticParallelMaster):
    def __init__(self, project, job_name):
        super(GammaSurface, self).__init__(project, job_name)
        self.__name__ = "GammaSurface"
        self.__version__ = "0.0.1"
        self.input["n_mesh_x"] = 10
        self.input["n_mesh_y"] = 10
        self._job_generator = GammaJobGenerator(self)
        self._output = {}
    def collect_output(self):
        if self.server.run_mode.interactive:
            ham = self.project_hdf5.inspect(self.child_ids[0])
            erg_lst = ham["output/generic/energy_tot"]
            _, x_lst, y_lst = zip(*self._job_generator.parameter_list)
            erg_lst, x_lst, y_lst = [], [], []
            for job_id in self.child_ids:
                ham = self.project_hdf5.inspect(job_id)
                job_name = ham.job_name
                x_lst.append(float(job_name.split("_y_")[0].split("x_")[1].replace('_', '.')))
                y_lst.append(float(job_name.split("_y_")[1].replace('_', '.')))
        self._output["energy"] = erg_lst
        self._output["x"] = x_lst
        self._output["y"] = y_lst
        with"output") as hdf5_out:
            for key, val in self._output.items():
                hdf5_out[key] = val
    def plot(self):
        if len(self._output) > 0:
            plt.plot(self._output["y"], self._output["energy"], 'x-'); 
    def plot2d(self):
        plt.imshow(np.reshape(self._output["energy"], (self.input["n_mesh"][0],-1)))

Example Project

To demonstrate the useage of the newly implemented class we create a small example project.

In [6]:
pr = Project("Gamma_parallel")


We use interactive LAMMPS jobs and calculate the gamma surface for two fcc crystal orientations namely 111 and 100.

In [8]:
surface_list = ['fcc111', 'fcc100']
fig, ax_list = plt.subplots(ncols=2, nrows=1, sharex=True)

potential = 'Al_Mg_Mendelev_eam'
for i, surf in enumerate(surface_list):
    with as pr_test:
        ax= ax_list[i]
        Al = pr_test.create_surface('Al', surf, (1,2,12), vacuum=10, orthogonal=True)
        ref_job = pr_test.create_job(pr_test.job_type.Lammps, 'ref_job')
        ref_job.structure = Al
        ref_job.potential = potential
        ref_job.interactive_enforce_structure_reset = True
        ref_job.server.run_mode.interactive = True
        gs = ref_job.create_job(GammaSurface, "gamma_" + surf)
        gs.input["n_mesh_x"] = 5
        gs.input["n_mesh_y"] = 19
        ax.contourf(np.reshape(gs._output["energy"], (gs.input["n_mesh_x"],-1)))
The job gamma_fcc111 was saved and received the ID: 1
The job gamma_fcc111_ref_job was saved and received the ID: 2
The job gamma_fcc100 was saved and received the ID: 3
The job gamma_fcc100_ref_job was saved and received the ID: 4
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