Merge pull request #7 from Crespo-Otero-group/dev

Prepare for a new release

doc/source/overview.rst

@@ -31,7 +31,7 @@ Installation
 .. code-block:: bash
 
    cd /path/to/dir/
-   git clone https://github.research.its.qmul.ac.uk/btx156/fromage.git
+   git clone https://github.com/Crespo-Otero-group/fromage.git
    cd fromage/
 
 2. Install

doc/source/script.rst

@@ -170,6 +170,30 @@ using either TDDFT or CIS. Make sure that all of the atoms of one molecule (A)
 appear before all of the atoms of the second molecule (B). Also make sure to
 print the ``.rwf`` file by using the input tag ``%rwf=filename.rwf``.
 
+Here is an example Gaussian input for a range-separated hybrid TDDFT calculation
+up to the fourth excited state:
+
+.. code-block:: html
+
+    %chk=title.chk
+    %mem=[X]GB
+    %nproc=[X]
+    #p wb97xd 6-31g* gfprint pop=full
+
+    title
+
+    0 1
+         H   0.00 0.00 0.00
+         C   0.00 0.00 0.00
+         .
+         .
+         .
+
+    --link1--
+    %chk=title.chk
+    %rwf=title
+    #p wb97xd 6-31g* td(nstates=4,root=4) gfprint pop=full IOP(3/33=3) geom=allcheck guess=read density=current
+
 Now run:
 
 .. code-block:: bash

doc/source/tutorial.rst

@@ -118,9 +118,10 @@ Input
 These are all the files needed for the geometry optimisation. Most of them
 were already generated from the previous step.
 
-* ``fromage.in``
+* ``fromage.in`` (not required for this tutorial)
     The input file which contains the specifications for the geometry
-    optimisation
+    optimisation. This tutorial uses default specifications, so this is not
+    required
 
 * ``mol.init.xyz``
     See above

fromage/utils/dimer.py

@@ -28,7 +28,7 @@ class Dimer(object):
 
     Attributes
     ----------
-    mols : list of two Mol objects
+    mol_a,mol_b : Mol objects
         The two molecules constituting the dimer
     alpha, beta, gamma : floats
         The describing angles. alpha is the angle between principal axes,

fromage/utils/mol/_cell_operations.py

@@ -165,34 +165,31 @@ def trans_from_rad(self, clust_rad):
         The translations required for the unit cell to contain the sphere
 
     """
-
-    # determine how many unit cells we need
-    vectors = deepcopy(self.vectors)
-
     # vectors normal to faces
-    a_perp = np.cross(vectors[1], vectors[2])
-    b_perp = np.cross(vectors[2], vectors[0])
-    c_perp = np.cross(vectors[0], vectors[1])
+    a_perp = np.cross(self.vectors[1], self.vectors[2])
+    b_perp = np.cross(self.vectors[2], self.vectors[0])
+    c_perp = np.cross(self.vectors[0], self.vectors[1])
 
     # the three normalised unit vectors
     perp = np.array([a_perp / np.linalg.norm(a_perp), b_perp /
                      np.linalg.norm(b_perp), c_perp / np.linalg.norm(c_perp)])
 
-    trans_count = np.array([1, 1, 1])
+    # dimensions of the final supercell (translations)
+    trans_count = np.array([0, 0, 0])
 
-    # distances from faces
-    distances = np.array([0.0, 0.0, 0.0])
+    # lattice vectors of the quadrant supercell
+    supercell_vectors = np.zeros((3,3))
 
-    new_vectors = deepcopy(vectors)
+    # distances from origin to each face
+    distances = np.array([0.0, 0.0, 0.0])
 
+    # loop over lattice vectors
     for comp in range(3):
-        while True:
+        while distances[comp] <= clust_rad:
             trans_count[comp] += 1
-            distances[comp] = np.dot(new_vectors[comp], perp[comp])
-            new_vectors[comp] = trans_count[comp] * vectors[comp]
-            if distances[comp] > clust_rad:
-                break
-    trans_count -= np.array([1, 1, 1])
+            supercell_vectors[comp] = trans_count[comp] * self.vectors[comp]
+            distances[comp] = np.dot(supercell_vectors[comp], perp[comp])
+
     return trans_count
 
 def supercell_for_cluster(self, clust_rad, mode='exc', central_mol=None):
@@ -371,6 +368,11 @@ def make_cluster(self, clust_rad, mode='exc', central_mol=None):
     # seed_atoms will initialise the cluster
     seed_atoms = mol_init.Mol([])
 
+    # conserve the bonding properties of the original cell
+    seed_atoms.bonding = supercell.bonding
+    seed_atoms.thresh = supercell.thresh
+
+
     # get seed atoms in the shape of the central mol if pertinent
     if central_mol:
         for atom_i in supercell:
@@ -385,8 +387,6 @@ def make_cluster(self, clust_rad, mode='exc', central_mol=None):
             if atom.v_dist([0, 0, 0]) < clust_rad:
                 seed_atoms.append(atom)
 
-    # refine seed atoms
-    max_mol_len = 0
     # remove incomplete molecules
     if mode == 'exc':
         clust_atoms = self.gen_exclusive_clust(seed_atoms)

tutorial/config

@@ -16,7 +16,7 @@ nAt 500
 aN 2
 bN 2
 cN 2
-clust_rad 7
+clust_rad 3
 traAN 3
 traBN 3
 traCN 3