Creating Grain Boundaries#

In this notebook, creation and querying of GBs are discussed. These features are still under active development.

from atomrdf import KnowledgeGraph
import atomrdf.build as build

from ase.visualize import view

kg = KnowledgeGraph()

We start by creating a \(\Sigma 5 (3 \bar{1} 0)\)

struct_gb_1 = build.defect.grain_boundary(axis=[0,0,1], 
                        sigma=5, 
                        gb_plane=[3, -1, 0],
                        element='Fe',
                        graph=kg)

We can visualise the structure

view(struct_gb_1, viewer='x3d')
ASE atomic visualization

Some other examples, \(\Sigma 3 (1\bar{-1}0)\) and \(\Sigma 19 (111)\)

struct_gb_2 = build.defect.grain_boundary(axis=[1,1,2], 
                        sigma=3, 
                        gb_plane=[1, -1, 0],
                        element='Fe',
                        graph=kg)

struct_gb_3 = build.defect.grain_boundary(axis=[1,1,1], 
                        sigma=19, 
                        gb_plane=[1, 1, 1],
                        element='Fe',
                        graph=kg)

What are all the samples with symmetric tilt grain boundaries?

res = kg.query_sample(kg.ontology.terms.pldo.SymmetricalTiltGrainBoundary)
res

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
Cell In[6], line 1
----> 1 res = kg.query_sample(kg.ontology.terms.pldo.SymmetricalTiltGrainBoundary)
      2 res

AttributeError: 'KnowledgeGraph' object has no attribute 'query_sample'

We see we have one structure in the database. We can also find what is the sigma value of this structure by modifying our query.

res = kg.query_sample([kg.ontology.terms.pldo.SymmetricalTiltGrainBoundary, 
                     kg.ontology.terms.pldo.hasSigmaValue])
res
AtomicScaleSample SymmetricalTiltGrainBoundary hasSigmaValuevalue
0 sample:4878c894-b017-4e05-be07-6669c450888f sample:4878c894-b017-4e05-be07-6669c450888f_Sy... 5

We can choose the sample, and save it

sample = res.AtomicScaleSample[0]
sample

rdflib.term.URIRef('sample:4878c894-b017-4e05-be07-6669c450888f')

kg.to_file(sample, filename="POSCAR", format="vasp")

! head -20 POSCAR

Fe
 1.0000000000000000
    18.1514737693664969   -0.0000000000000006    0.0000000000000000
     0.0000000000000000    9.0757368846832485    0.0000000000000000
     0.0000000000000011    0.0000000000000006    2.8699999999999997
 Fe 
  40
Cartesian
  0.0000000000000000  0.0000000000000000  0.0000000000000000
  2.7227210654049743  0.9075736884683246  0.0000000000000002
  1.8151473769366497  3.6302947538732990  0.0000000000000003
  0.9075736884683242  6.3530158192782737  0.0000000000000004
  5.4454421308099485  1.8151473769366493  0.0000000000000004
  4.5378684423416242  4.5378684423416233  0.0000000000000006
  3.6302947538732995  7.2605895077465981  0.0000000000000007
  8.1681631962149215  2.7227210654049738  0.0000000000000007
  7.2605895077465972  5.4454421308099477  0.0000000000000008
  6.3530158192782693  8.1681631962149215  0.0000000000000009
  0.9075736884683249  1.8151473769366495  1.4350000000000001
  3.6302947538732990  2.7227210654049743  1.4350000000000003
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