def zmatrix_with_conversion_info(geo, ts_bnds=()):
""" Generate a corresponding Z-Matrix for a molecular geometry
using internal autochem procedures.
:param geo: molecular geometry
:type geo: automol geometry data structure
:param ts_bnds: keys for the breaking/forming bonds in a TS
:type ts_bnds: tuple(frozenset(int))
:returns: automol Z-Matrix data structure, Z-Matrix atom ordering, and
a dictionary mapping linear atoms onto their associated dummy atoms
"""
if ts_bnds:
raise NotImplementedError
if is_atom(geo):
symbs = symbols(geo)
key_mat = [[None, None, None]]
val_mat = [[None, None, None]]
zma = automol.zmat.base.from_data(symbs, key_mat, val_mat)
zma_keys = [0]
dummy_key_dct = {}
else:
geo, dummy_key_dct = insert_dummies_on_linear_atoms(geo)
gra = connectivity_graph(geo)
bnd_keys = tuple(dummy_key_dct.items())
ord_dct = {k: 0 for k in bnd_keys}
gra = automol.graph.add_bonds(gra, bnd_keys, ord_dct=ord_dct)
vma, zma_keys = automol.graph.vmat.vmatrix(gra)
geo = from_subset(geo, zma_keys)
zma = automol.zmat.base.from_geometry(vma, geo)
return zma, zma_keys, dummy_key_dct
def connectivity_graph(geo,
rqq_bond_max=3.45,
rqh_bond_max=2.6,
rhh_bond_max=1.9):
""" Generate a molecular graph from the molecular geometry that has information
about bond connectivity.
:param rqq_bond_max: maximum distance between heavy atoms
:type rqq_bond_max: float
:param rqh_bond_max: maximum distance between heavy atoms and hydrogens
:type rqh_bond_max: float
:param rhh_bond_max: maximum distance between hydrogens
:type rhh_bond_max: float
:rtype: automol molecular graph structure
"""
symbs = symbols(geo)
xyzs = coordinates(geo)
def _distance(idx_pair):
xyz1, xyz2 = map(xyzs.__getitem__, idx_pair)
dist = numpy.linalg.norm(numpy.subtract(xyz1, xyz2))
return dist
def _are_bonded(idx_pair):
sym1, sym2 = map(symbs.__getitem__, idx_pair)
dist = _distance(idx_pair)
return (False if 'X' in (sym1, sym2) else
(dist < rqh_bond_max) if 'H' in (sym1, sym2) else
(dist < rhh_bond_max) if (sym1 == 'H' and sym2 == 'H') else
(dist < rqq_bond_max))
idxs = range(len(xyzs))
atm_symb_dct = dict(enumerate(symbs))
bnd_keys = tuple(
map(frozenset, filter(_are_bonded, itertools.combinations(idxs, r=2))))
bnd_ord_dct = {bnd_key: 1 for bnd_key in bnd_keys}
gra = automol.graph.from_data(atm_symb_dct=atm_symb_dct,
bnd_keys=bnd_keys,
bnd_ord_dct=bnd_ord_dct)
return gra
def x2z_atom_ordering(geo, ts_bnds=()):
""" Generate a dictionary which maps the order of atoms from the input
molecular geometry to the order of atoms of the resulting Z-Matrix
that is generated by the x2z interface.
:param geo: molecular geometry
:type geo: automol geometry data structure
:param ts_bnds: keys for the breaking/forming bonds in a TS
:type ts_bnds: tuple(frozenset(int))
:rtype: dict[int: int]
"""
symbs = symbols(geo)
if len(symbs) == 1:
idxs = {0: 0}
else:
x2m = _pyx2z.from_geometry(geo, ts_bnds=ts_bnds)
idxs = _pyx2z.zmatrix_atom_ordering(x2m)
return idxs
def x2z_torsion_coordinate_names(geo, ts_bnds=()):
""" Generate a list of torsional coordinates using x2z interface. These
names corresond to the Z-Matrix generated using the same algorithm.
:param geo: molecular geometry
:type geo: automol geometry data structure
:param ts_bnds: keys for the breaking/forming bonds in a TS
:type ts_bnds: tuple(frozenset(int))
:rtype: tuple(str)
"""
symbs = symbols(geo)
if len(symbs) == 1:
names = ()
else:
x2m = _pyx2z.from_geometry(geo, ts_bnds=ts_bnds)
names = _pyx2z.zmatrix_torsion_coordinate_names(x2m)
zma = _pyx2z.to_zmatrix(x2m)
name_dct = automol.zmat.base.standard_names(zma)
names = tuple(map(name_dct.__getitem__, names))
return names
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 _ts_compare(ref_zma, zma, zrxn):
""" Perform a series of checks to assess the viability
of a transition state geometry prior to saving
"""
# Initialize viable
viable = True
# Get the bond dists and calculate the distance of bond being formed
ref_geo = automol.zmat.geometry(ref_zma)
cnf_geo = automol.zmat.geometry(zma)
grxn = automol.reac.relabel_for_geometry(zrxn)
frm_bnd_keys = automol.reac.forming_bond_keys(grxn)
brk_bnd_keys = automol.reac.breaking_bond_keys(grxn)
cnf_dist_lst = []
ref_dist_lst = []
bnd_key_lst = []
cnf_ang_lst = []
ref_ang_lst = []
for frm_bnd_key in frm_bnd_keys:
frm_idx1, frm_idx2 = list(frm_bnd_key)
cnf_dist = distance(cnf_geo, frm_idx1, frm_idx2)
ref_dist = distance(ref_geo, frm_idx1, frm_idx2)
cnf_dist_lst.append(cnf_dist)
ref_dist_lst.append(ref_dist)
bnd_key_lst.append(frm_bnd_key)
for brk_bnd_key in brk_bnd_keys:
brk_idx1, brk_idx2 = list(brk_bnd_key)
cnf_dist = distance(cnf_geo, brk_idx1, brk_idx2)
ref_dist = distance(ref_geo, brk_idx1, brk_idx2)
cnf_dist_lst.append(cnf_dist)
ref_dist_lst.append(ref_dist)
bnd_key_lst.append(brk_bnd_key)
for frm_bnd_key in frm_bnd_keys:
for brk_bnd_key in brk_bnd_keys:
for frm_idx in frm_bnd_key:
for brk_idx in brk_bnd_key:
if frm_idx == brk_idx:
idx2 = frm_idx
idx1 = list(frm_bnd_key - frozenset({idx2}))[0]
idx3 = list(brk_bnd_key - frozenset({idx2}))[0]
cnf_ang = central_angle(cnf_geo, idx1, idx2, idx3)
ref_ang = central_angle(ref_geo, idx1, idx2, idx3)
cnf_ang_lst.append(cnf_ang)
ref_ang_lst.append(ref_ang)
# print('bnd_key_list', bnd_key_lst)
# print('conf_dist', cnf_dist_lst)
# print('ref_dist', ref_dist_lst)
# print('conf_angle', cnf_ang_lst)
# print('ref_angle', ref_ang_lst)
# Set the maximum allowed displacement for a TS conformer
max_disp = 0.6
# better to check for bond-form length in bond scission with ring forming
if 'addition' in grxn.class_:
max_disp = 0.8
if 'abstraction' in grxn.class_:
# this was 1.4 - SJK reduced it to work for some OH abstractions
max_disp = 1.0
# Check forming bond angle similar to ini config
if 'elimination' not in grxn.class_:
for ref_angle, cnf_angle in zip(ref_ang_lst, cnf_ang_lst):
if abs(cnf_angle - ref_angle) > .44:
print('angle', ref_angle, cnf_angle)
viable = False
symbs = symbols(cnf_geo)
lst_info = zip(ref_dist_lst, cnf_dist_lst, bnd_key_lst)
for ref_dist, cnf_dist, bnd_key in lst_info:
if 'add' in grxn.class_ or 'abst' in grxn.class_:
bnd_key1, bnd_key2 = min(list(bnd_key)), max(list(bnd_key))
symb1 = symbs[bnd_key1]
symb2 = symbs[bnd_key2]
if bnd_key in frm_bnd_keys:
# Check if radical atom is closer to some atom
# other than the bonding atom
cls = automol.zmat.base.is_atom_closest_to_bond_atom(
zma, bnd_key2, cnf_dist)
if not cls:
print('distance', ref_dist, cnf_dist)
print(' - Radical atom now has a new nearest neighbor')
viable = False
# check forming bond distance
if abs(cnf_dist - ref_dist) > max_disp:
print('distance', ref_dist, cnf_dist)
viable = False
# Check distance relative to equi. bond
equi_bnd = dict_.values_by_unordered_tuple(bnd.LEN_DCT,
(symb1, symb2),
fill_val=0.0)
displace_from_equi = cnf_dist - equi_bnd
dchk1 = abs(cnf_dist - ref_dist) > 0.1
dchk2 = displace_from_equi < 0.2
if dchk1 and dchk2:
print(cnf_dist, equi_bnd)
viable = False
else:
# check forming/breaking bond distance
# if abs(cnf_dist - ref_dist) > 0.4:
# max disp of 0.4 causes problems for bond scission w/ ring forming
# not sure if setting it to 0.3 will cause problems for other cases
if abs(cnf_dist - ref_dist) > 0.3:
print('distance', ref_dist, cnf_dist)
viable = False
return viable
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
