def conf_is_unique_rmsd(conf, conf_list, rmsd_tol=None):
"""
Determine if a conformer is unique based on an root mean squared
displacement RMSD threshold based on heavy atoms
Arguments:
conf (autode.conformer.Conformer):
conf_list (list((list(autode.conformer.Conformer)):
Keyword Arguments:
rmsd_tol (float): Tolerance for an equivalent structure based on the
rmsd in Å. If None then use the default value for
autode.Config.rmsd_threshold
Returns:
(bool):
"""
rmsd_tol = Config.rmsd_threshold if rmsd_tol is None else rmsd_tol
logger.info(f'Removing conformers with RMSD < {rmsd_tol} Å to any other')
# Calculate the RMSD between this Conformer and the those in conf_list
# using the Kabsch algorithm
for other_conf in conf_list:
if calc_heavy_atom_rmsd(conf.atoms, other_conf.atoms) < rmsd_tol:
return False
return True
def test_calc_rmsd():
atoms = [
Atom('C', 0.0009, 0.0041, -0.0202),
Atom('H', -0.6577, -0.8481, -0.3214),
Atom('H', -0.4585, 0.9752, -0.3061),
Atom('H', 0.0853, -0.0253, 1.0804),
Atom('H', 1.0300, -0.1058, -0.4327)
]
atoms_rot = [
Atom('C', -0.0009, -0.0041, -0.0202),
Atom('H', 0.6577, 0.8481, -0.3214),
Atom('H', 0.4585, -0.9752, -0.3061),
Atom('H', -0.0853, 0.0253, 1.0804),
Atom('H', -1.0300, 0.1058, -0.4327)
]
coords1 = np.array([atom.coord for atom in atoms])
coords2 = np.array([atom.coord for atom in atoms_rot])
# Rotated coordinates should have almost 0 RMSD between them
assert geom.calc_rmsd(coords1, coords2) < 1E-5
# Coordinates need to have the same shape to calculate the RMSD
with pytest.raises(AssertionError):
_ = geom.calc_rmsd(coords1, coords2[1:])
assert geom.calc_heavy_atom_rmsd(atoms, atoms_rot) < 1E-5
# Permuting two hydrogens should generate a larger RMSD
atoms_rot[2], atoms_rot[3] = atoms_rot[3], atoms_rot[2]
rmsd = geom.calc_rmsd(coords1=np.array([atom.coord for atom in atoms]),
coords2=np.array([atom.coord for atom in atoms_rot]))
assert rmsd > 0.1
# While the heavy atom RMSD should remain unchanged
assert geom.calc_heavy_atom_rmsd(atoms, atoms_rot) < 1E-6
def find_lowest_energy_ts_conformer(self, rmsd_threshold=None):
"""Find the lowest energy transition state conformer by performing
constrained optimisations"""
logger.info('Finding lowest energy TS conformer')
atoms, energy = deepcopy(self.atoms), deepcopy(self.energy)
calc = deepcopy(self.optts_calc)
hmethod = get_hmethod() if Config.hmethod_conformers else None
self.find_lowest_energy_conformer(hmethod=hmethod)
# Remove similar TS conformer that are similar to this TS based on root
# mean squared differences in their structures
thresh = Config.rmsd_threshold if rmsd_threshold is None else rmsd_threshold
self.conformers = [
conf for conf in self.conformers
if calc_heavy_atom_rmsd(conf.atoms, atoms) > thresh
]
logger.info(f'Generated {len(self.conformers)} unique (RMSD > '
f'{thresh} Å) TS conformer(s)')
# Optimise the lowest energy conformer to a transition state - will
# .find_lowest_energy_conformer will have updated self.atoms etc.
if len(self.conformers) > 0:
self.optimise(name_ext='optts_conf')
if self.is_true_ts() and self.energy < energy:
logger.info('Conformer search successful')
return None
# Ensure the energy has a numerical value, so a difference can be
# evaluated
self.energy = self.energy if self.energy is not None else 0
logger.warning(f'Transition state conformer search failed '
f'(∆E = {energy - self.energy:.4f} Ha). Reverting')
logger.info('Reverting to previously found TS')
self.atoms = atoms
self.energy = energy
self.optts_calc = calc
self.imaginary_frequencies = calc.get_imaginary_freqs()
return None