def linear_atoms(geo, gra=None, tol=5.):
""" find linear atoms in a geometry (atoms with 180 degree bond angle)
:param geo: the geometry
:type geo: automol geometry data structure
:param gra: the graph describing connectivity; if None, a connectivity
graph will be generated using default distance thresholds
:type gra: automol graph data structure
:param tol: the tolerance threshold for linearity, in degrees
:type tol: float
:rtype: tuple(int)
"""
gra = connectivity_graph(geo) if gra is None else gra
ngb_idxs_dct = automol.graph.atoms_neighbor_atom_keys(gra)
lin_idxs = []
for idx in range(count(geo)):
nidxs = ngb_idxs_dct[idx]
if len(nidxs) >= 2:
for nidx1, nidx2 in itertools.combinations(nidxs, 2):
ang = central_angle(geo, nidx1, idx, nidx2, degree=True)
if numpy.abs(ang - 180.) < tol:
lin_idxs.append(idx)
lin_idxs = tuple(lin_idxs)
return lin_idxs
def hydrogen_bonded_idxs(geo, dist_thresh=5.3, angle_thresh=1.92, grxn=None):
""" Compare bond lengths in structure to determine if there
is a hydrogen bond.
:param geo: geometry object
:type geo: geo object (tuple of tuples)
:param grxn: reaction object (geo indexing)
:type grxn: automol.reac.Reaction object
:param dist_thresh: cutoff value for hbond length (Bohr)
:type dist_thresh: float
:param angle_thresh: cutoff value for hbond angle (Radian)
:type angle_thresh: float
:rtype: tuple
"""
# Initialize the hydrogen bond list to None
hydrogen_bond = None
if count(geo) > 1:
# Get the forming/breaking bond idxs if possible
if grxn is not None:
frm_bnd_keys = automol.graph.ts.forming_bond_keys(
grxn.forward_ts_graph)
brk_bnd_keys = automol.graph.ts.breaking_bond_keys(
grxn.forward_ts_graph)
rxn_keys = set()
for key in frm_bnd_keys:
rxn_keys = rxn_keys | key
for key in brk_bnd_keys:
rxn_keys = rxn_keys | key
rxn_h_idxs = tuple(rxn_keys)
else:
rxn_h_idxs = ()
# Get all potential indices for HB interactions
gra = graph(geo)
dist_mat = distance_matrix(geo)
adj_atm_dct = automol.graph.atoms_neighbor_atom_keys(gra)
h_idxs = automol.graph.atom_keys(gra, sym='H')
acceptor_idxs = list(
automol.graph.resonance_dominant_radical_atom_keys(gra))
acceptor_idxs.extend(list(automol.graph.atom_keys(gra, sym='O')))
# Loop over indices, ignoring H-idxs in reacting bonds
hb_idxs = tuple(idx for idx in h_idxs if idx not in rxn_h_idxs)
for h_idx in hb_idxs:
for acceptor_idx in acceptor_idxs:
donor_idx = list(adj_atm_dct[h_idx])[0]
if acceptor_idx in adj_atm_dct[donor_idx]:
continue
if dist_mat[h_idx][acceptor_idx] < dist_thresh:
ang = central_angle(geo, donor_idx, h_idx, acceptor_idx)
if ang > angle_thresh:
hydrogen_bond = (
donor_idx,
h_idx,
acceptor_idx,
)
dist_thresh = dist_mat[h_idx][acceptor_idx]
return hydrogen_bond
def angle_distances(geos, angstrom=True):
""" return a dictionary of distances between ends of a bond angle for a
sequence of reactant geometries, shifting the keys as needed
:param geos: the reactant geometries
:param angstrom: return the distances in angstroms?
"""
dist_dct = {}
shift = 0
for geo in geos:
gra = connectivity_graph(geo)
pairs = [(k1, k3) for k1, _, k3 in automol.graph.angle_keys(gra)]
keys = [frozenset({k1 + shift, k2 + shift}) for k1, k2 in pairs]
dists = [distance(geo, *p, angstrom=angstrom) for p in pairs]
dist_dct.update(dict(zip(keys, dists)))
shift += count(geo)
return dist_dct
def inchi_with_sort(geo, stereo=True):
""" Generate an InChI string from a molecular geometry. (Sort?)
:param geo: molecular geometry
:type geo: automol geometry data structure
:param stereo: parameter to include stereochemistry information
:type stereo: bool
:rtype: str
"""
ich = automol.inchi.base.hardcoded_object_to_inchi_by_key(
'geom', geo, comp=_compare)
nums_lst = None
if ich is None:
gra = connectivity_graph(geo)
if not stereo:
geo = None
geo_idx_dct = None
else:
geo_idx_dct = dict(enumerate(range(count(geo))))
ich, nums_lst = automol.graph.inchi_with_sort_from_geometry(
gra=gra, geo=geo, geo_idx_dct=geo_idx_dct)
return ich, nums_lst
def join(geo1,
geo2,
key2,
key3,
r23,
a123=85.,
a234=85.,
d1234=85.,
key1=None,
key4=None,
angstrom=True,
degree=True):
""" join two geometries based on four of their atoms, two on the first
and two on the second
Variables set the coordinates for 1-2...3-4 where 1-2 are bonded atoms in
geo1 and 3-4 are bonded atoms in geo2.
"""
key3 = key3 - count(geo1)
a123 *= phycon.DEG2RAD if degree else 1
a234 *= phycon.DEG2RAD if degree else 1
d1234 *= phycon.DEG2RAD if degree else 1
gra1, gra2 = map(connectivity_graph, (geo1, geo2))
key1 = (automol.graph.atom_neighbor_atom_key(gra1, key2)
if key1 is None else key1)
key4 = (automol.graph.atom_neighbor_atom_key(gra2, key3)
if key4 is None else key4)
syms1 = symbols(geo1)
syms2 = symbols(geo2)
xyzs1 = coordinates(geo1, angstrom=angstrom)
xyzs2 = coordinates(geo2, angstrom=angstrom)
xyz1 = xyzs1[key1]
xyz2 = xyzs1[key2]
orig_xyz3 = xyzs2[key3]
if key4 is not None:
orig_xyz4 = xyzs2[key4]
else:
orig_xyz4 = [1., 1., 1.]
r34 = vec.distance(orig_xyz3, orig_xyz4)
# Place xyz3 as far away from the atoms in geo1 as possible by optimizing
# the undetermined dihedral angle
xyz0 = vec.arbitrary_unit_perpendicular(xyz2, orig_xyz=xyz1)
def _distance_norm(dih): # objective function for minimization
dih, = dih
xyz3 = vec.from_internals(dist=r23,
xyz1=xyz2,
ang=a123,
xyz2=xyz1,
dih=dih,
xyz3=xyz0)
dist_norm = numpy.linalg.norm(numpy.subtract(xyzs1, xyz3))
# return the negative norm so that minimum value gives maximum distance
return -dist_norm
res = scipy.optimize.basinhopping(_distance_norm, 0.)
dih = res.x[0]
# Now, get the next position with the optimized dihedral angle
xyz3 = vec.from_internals(dist=r23,
xyz1=xyz2,
ang=a123,
xyz2=xyz1,
dih=dih,
xyz3=xyz0)
# Don't use the dihedral angle if 1-2-3 are linear
if numpy.abs(a123 * phycon.RAD2DEG - 180.) > 5.:
xyz4 = vec.from_internals(dist=r34,
xyz1=xyz3,
ang=a234,
xyz2=xyz2,
dih=d1234,
xyz3=xyz1)
else:
xyz4 = vec.from_internals(dist=r34, xyz1=xyz3, ang=a234, xyz2=xyz2)
align_ = vec.aligner(orig_xyz3, orig_xyz4, xyz3, xyz4)
xyzs2 = tuple(map(align_, xyzs2))
syms = syms1 + syms2
xyzs = xyzs1 + xyzs2
geo = from_data(syms, xyzs, angstrom=angstrom)
return geo
def insert_dummies_on_linear_atoms(geo,
lin_idxs=None,
gra=None,
dist=1.,
tol=5.):
""" Insert dummy atoms over linear atoms in the geometry.
:param geo: the geometry
:type geo: automol molecular geometry data structure
:param lin_idxs: the indices of the linear atoms; if None, indices are
automatically determined from the geometry based on the graph
:type lin_idxs: tuple(int)
:param gra: the graph describing connectivity; if None, a connectivity
graph will be generated using default distance thresholds
:type gra: automol molecular graph data structure
:param dist: distance of dummy atom from the linear atom, in angstroms
:type dist: float
:param tol: the tolerance threshold for linearity, in degrees
:type tol: float
:returns: geometry with dummy atoms inserted, along with a dictionary
mapping the linear atoms onto their associated dummy atoms
:rtype: automol molecular geometry data structure
"""
lin_idxs = linear_atoms(geo) if lin_idxs is None else lin_idxs
gra = connectivity_graph(geo) if gra is None else gra
dummy_ngb_idxs = set(
automol.graph.dummy_atoms_neighbor_atom_key(gra).values())
assert not dummy_ngb_idxs & set(lin_idxs), (
"Attempting to add dummy atoms on atoms that already have them: {}".
format(dummy_ngb_idxs & set(lin_idxs)))
ngb_idxs_dct = automol.graph.atoms_sorted_neighbor_atom_keys(gra)
xyzs = coordinates(geo, angstrom=True)
def _perpendicular_direction(idxs):
""" find a nice perpendicular direction for a series of linear atoms
"""
triplets = []
for idx in idxs:
for n1idx in ngb_idxs_dct[idx]:
for n2idx in ngb_idxs_dct[n1idx]:
if n2idx != idx:
ang = central_angle(geo,
idx,
n1idx,
n2idx,
degree=True)
if numpy.abs(ang - 180.) > tol:
triplets.append((idx, n1idx, n2idx))
if triplets:
idx1, idx2, idx3 = min(triplets, key=lambda x: x[1:])
xyz1, xyz2, xyz3 = map(xyzs.__getitem__, (idx1, idx2, idx3))
r12 = util.vec.unit_direction(xyz1, xyz2)
r23 = util.vec.unit_direction(xyz2, xyz3)
direc = util.vec.orthogonalize(r12, r23, normalize=True)
else:
if len(idxs) > 1:
idx1, idx2 = idxs[:2]
else:
idx1, = idxs
idx2, = ngb_idxs_dct[idx1]
xyz1, xyz2 = map(xyzs.__getitem__, (idx1, idx2))
r12 = util.vec.unit_direction(xyz1, xyz2)
for i in range(3):
disp = numpy.zeros((3, ))
disp[i] = -1.
alt = numpy.add(r12, disp)
direc = util.vec.unit_perpendicular(r12, alt)
if numpy.linalg.norm(direc) > 1e-2:
break
return direc
# partition the linear atoms into adjacent groups, to be handled together
lin_idxs_lst = sorted(
map(
sorted,
util.equivalence_partition(lin_idxs,
lambda x, y: x in ngb_idxs_dct[y])))
dummy_key_dct = {}
for idxs in lin_idxs_lst:
direc = _perpendicular_direction(idxs)
for idx in idxs:
xyz = numpy.add(xyzs[idx], numpy.multiply(dist, direc))
dummy_key_dct[idx] = count(geo)
geo = insert(geo, 'X', xyz, angstrom=True)
return geo, dummy_key_dct
def join(geo1,
geo2,
key2,
key3,
r23,
a123=85.,
a234=85.,
d1234=85.,
key1=None,
key4=None,
angstrom=True,
degree=True):
""" join two geometries based on four of their atoms, two on the first
and two on the second
Variables set the coordinates for 1-2...3-4 where 1-2 are bonded atoms in
geo1 and 3-4 are bonded atoms in geo2.
"""
key3 = key3 - count(geo1)
a123 *= phycon.DEG2RAD if degree else 1
a234 *= phycon.DEG2RAD if degree else 1
d1234 *= phycon.DEG2RAD if degree else 1
gra1, gra2 = map(connectivity_graph, (geo1, geo2))
key1 = (automol.graph.atom_neighbor_atom_key(gra1, key2)
if key1 is None else key1)
key4 = (automol.graph.atom_neighbor_atom_key(gra2, key3)
if key4 is None else key4)
syms1 = symbols(geo1)
syms2 = symbols(geo2)
xyzs1 = coordinates(geo1, angstrom=angstrom)
xyzs2 = coordinates(geo2, angstrom=angstrom)
xyz1 = xyzs1[key1] if key1 is not None else None
xyz2 = xyzs1[key2]
orig_xyz3 = xyzs2[key3]
orig_xyz4 = xyzs2[key4] if key4 is not None else [1., 1., 1.]
if key1 is None:
# If the first fragment is monatomic, we can place the other one
# anywhere we want (direction doesn't matter)
xyz3 = numpy.add(xyz2, [0., 0., r23])
else:
# If the first fragment isn't monatomic, we need to take some care in
# where we place the second one.
# r23 and a123 are fixed, so the only degree of freedom we have to do
# this is to optimize a dihedral angle relative to an arbitrary point
# xyz0.
# This dihedral angle is optimized to maximize the distance of xyz3
# from all of the atoms in fragment 1 (xyzs1).
xyz1 = xyzs1[key1]
# Place xyz3 as far away from the atoms in geo1 as possible by
# optimizing the undetermined dihedral angle
xyz0 = vec.arbitrary_unit_perpendicular(xyz2, orig_xyz=xyz1)
def _distance_norm(dih): # objective function for minimization
dih, = dih
xyz3 = vec.from_internals(dist=r23,
xyz1=xyz2,
ang=a123,
xyz2=xyz1,
dih=dih,
xyz3=xyz0)
dist_norm = numpy.linalg.norm(numpy.subtract(xyzs1, xyz3))
# return the negative norm so that minimum value gives maximum
# distance
return -dist_norm
res = scipy.optimize.basinhopping(_distance_norm, 0.)
dih = res.x[0]
# Now, get the next position with the optimized dihedral angle
xyz3 = vec.from_internals(dist=r23,
xyz1=xyz2,
ang=a123,
xyz2=xyz1,
dih=dih,
xyz3=xyz0)
r34 = vec.distance(orig_xyz3, orig_xyz4)
# If 2 doen't have neighbors or 1-2-3 are linear, ignore the dihedral angle
if key1 is None or numpy.abs(a123 * phycon.RAD2DEG - 180.) < 5.:
xyz4 = vec.from_internals(dist=r34, xyz1=xyz3, ang=a234, xyz2=xyz2)
else:
xyz4 = vec.from_internals(dist=r34,
xyz1=xyz3,
ang=a234,
xyz2=xyz2,
dih=d1234,
xyz3=xyz1)
align_ = vec.aligner(orig_xyz3, orig_xyz4, xyz3, xyz4)
xyzs2 = tuple(map(align_, xyzs2))
syms = syms1 + syms2
xyzs = xyzs1 + xyzs2
geo = from_data(syms, xyzs, angstrom=angstrom)
return geo
