}
water = Molecule(name='H2O', smiles='O')
# For the three different DFT functionals calculate the PES and plot the line
for dft_name, keywords in keywords_list.items():
# Create arrays for OH distances and their energies
rs = np.linspace(0.65, 2.0, num=15)
energies = []
# Calculate the energy array
for r in rs:
o_atom, h_atom = water.atoms[:2]
curr_r = water.distance(0, 1)
vector = (h_atom.coord - o_atom.coord) * (r / curr_r - 1)
h_atom.translate(vector)
# Set up and run the calculation
calc = Calculation(name=f'H2O_scan_{r:.2f}',
molecule=water,
method=orca,
keywords=keywords)
calc.run()
# Get the potential energy from the calculation
energy = calc.get_energy()
energies.append(energy)
import numpy as np
# Initialise the electronic structure method (XTB)
xtb = XTB()
water = Molecule(name='H2O', smiles='O')
rs = np.linspace(0.65, 2.0, num=20)
# List of energies to be populated for the single point (unrelaxed)
# and constrained optimisations (relaxed) calculations
sp_energies, opt_energies = [], []
for r in rs:
o_atom, h_atom = water.atoms[:2]
curr_r = water.distance(0, 1) # current O-H distance
# Shift the hydrogen atom to the required distance
# vector = (h_atom.coord - o_atom.coord) / curr_r * (r - curr_r)
vector = (h_atom.coord - o_atom.coord) * (r / curr_r - 1)
h_atom.translate(vector)
# Set up and run the single point energy evaluation
sp = Calculation(name=f'H2O_scan_unrelaxed_{r:.2f}',
molecule=water,
method=xtb,
keywords=xtb.keywords.sp)
sp.run()
sp_energies.append(sp.get_energy())
# Set up the constrained optimisation calculation where the distance
from autode import Molecule
from autode.input_output import xyz_file_to_atoms
from autode.conformers import conf_gen, Conformer
from autode.methods import XTB
# Initialise the complex from a .xyz file containing a square planar structure
vaskas = Molecule(name='vaskas', atoms=xyz_file_to_atoms('vaskas.xyz'))
# Set up some distance constraints where the keys are the atom indexes and
# the value the distance in Å. Fixing the Cl-P, Cl-P and Cl-C(=O) distances
# enforces a square planar geometry
distance_constraints = {(1, 2): vaskas.distance(1, 2),
(1, 3): vaskas.distance(1, 3),
(1, 4): vaskas.distance(1, 4)}
# Generate 5 conformers
for n in range(5):
# Apply random displacements to each atom and minimise under a bonded +
# repulsive forcefield including the distance constraints
atoms = conf_gen.get_simanl_atoms(species=vaskas,
dist_consts=distance_constraints,
conf_n=n)
# Generate a conformer from these atoms then optimise with XTB
conformer = Conformer(name=f'vaskas_conf{n}', atoms=atoms)
conformer.optimise(method=XTB())
conformer.print_xyz_file()