Merge pull request #1044 from hillarypan/master

added BrunnerNN class that uses Brunner's method to calculate near neighbors

pymatgen/analysis/local_env.py

@@ -2008,3 +2008,57 @@ def get_order_parameters(self, structure, n, indices_neighs=None, \
                                                               0]) ** 2)
 
         return ops
+
+
+class BrunnerNN(NearNeighbors):
+    """
+    Determine coordination number using Brunner's algorithm which counts the
+    atoms that are within the largest gap in differences in real space
+    interatomic distances.
+
+    Note: Might be highly inaccurate in certain cases.
+
+    Args:
+        mode (str): type of neighbor-finding approach, where "reciprocal"
+            will use Brunner's method of largest reciprocal gap in
+            interatomic distances, "relative" will use Brunner's method of largest
+            relative gap in interatomic distances, and "real" will use Brunner's
+            method of largest gap in interatomic distances.
+            Defaults to "reciprocal" method.
+        tol (float): tolerance parameter for bond determination
+            (default: 1E-4).
+        cutoff (float): cutoff radius in Angstrom to look for near-neighbor
+            atoms. Defaults to 8.0.
+    """
+
+    def __init__(self, mode="reciprocal", tol=1.0e-4, cutoff=8.0):
+        self.mode = mode
+        self.tol = tol
+        self.cutoff = cutoff
+
+    def get_nn_info(self, structure, n):
+
+        site = structure[n]
+        neighs_dists = structure.get_neighbors(site, self.cutoff)
+        ds = [i[-1] for i in neighs_dists]
+        ds.sort()
+
+        if self.mode == "reciprocal":
+            ns = [1.0 / ds[i] - 1.0 / ds[i + 1] for i in range(len(ds) - 1)]
+        elif self.mode == "relative":
+            ns = [ds[i] / ds[i + 1] for i in range(len(ds) - 1)]
+        elif self.mode == "real":
+            ns = [ds[i] - ds[i + 1] for i in range(len(ds) - 1)]
+        else:
+            raise ValueError("Unknown Brunner CN mode.")
+
+        d_max = ds[ns.index(max(ns))]
+        siw = []
+        for s, dist in neighs_dists:
+            if dist < d_max + self.tol:
+                w = ds[0] / dist
+                siw.append({'site': s,
+                            'image': self._get_image(s.frac_coords),
+                            'weight': w,
+                            'site_index': self._get_original_site(structure, s)})
+        return siw

pymatgen/analysis/tests/test_local_env.py

@@ -12,7 +12,7 @@
         VoronoiNN, JMolNN, \
         MinimumDistanceNN, MinimumOKeeffeNN, MinimumVIRENN, \
         get_neighbors_of_site_with_index, site_is_of_motif_type, \
-        NearNeighbors, LocalStructOrderParas
+        NearNeighbors, LocalStructOrderParas, BrunnerNN
 from pymatgen import Element, Structure, Lattice
 from pymatgen.util.testing import PymatgenTest
 from pymatgen.io.cif import CifParser
@@ -157,6 +157,27 @@ def test_all_nn_classes(self):
         self.assertAlmostEqual(MinimumVIRENN(tol=0.01).get_cn(
             self.cscl, 0), 8)
 
+        self.assertAlmostEqual(BrunnerNN(tol=0.01).get_cn(
+            self.diamond, 0), 4)
+        self.assertAlmostEqual(BrunnerNN(tol=0.01).get_cn(
+            self.nacl, 0), 6)
+        self.assertAlmostEqual(BrunnerNN(tol=0.01).get_cn(
+            self.cscl, 0), 14)
+
+        self.assertAlmostEqual(BrunnerNN(mode="real", tol=0.01).get_cn(
+            self.diamond, 0), 16)
+        self.assertAlmostEqual(BrunnerNN(mode="real", tol=0.01).get_cn(
+            self.nacl, 0), 18)
+        self.assertAlmostEqual(BrunnerNN(mode="real", tol=0.01).get_cn(
+            self.cscl, 0), 8)
+
+        self.assertAlmostEqual(BrunnerNN(mode="relative", tol=0.01).get_cn(
+            self.diamond, 0), 16)
+        self.assertAlmostEqual(BrunnerNN(mode="relative", tol=0.01).get_cn(
+            self.nacl, 0), 18)
+        self.assertAlmostEqual(BrunnerNN(mode="relative", tol=0.01).get_cn(
+            self.cscl, 0), 8)
+
     def tearDown(self):
         del self.diamond
         del self.nacl