def test_solvent():
methane = Molecule(smiles='C')
methane.solvent = 'water'
# Calculation should be able to handle a solvent given as just a string
assert get_solvent_name(molecule=methane, method=mopac) == 'water'
# Unknown solvent should raise an exception
with pytest.raises(SolventNotFound):
methane.solvent = 'XXXX'
get_solvent_name(molecule=methane, method=mopac)
def test_dft():
orca = ORCA()
methods.clear()
h2 = Molecule(smiles='[H][H]', solvent_name='water')
h2.single_point(method=orca)
assert 'PBE0' in str(methods)
assert 'def2-TZVP' in str(methods)
assert '4.2.1' in str(methods)
# Default CPCM solvation in orca
assert 'CPCM' in str(methods)
from autode import Molecule
from autode.calculation import Calculation
from autode.methods import XTB
import matplotlib.pyplot as plt
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.get_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)
from autode import Molecule, Config from autode.species import NCIComplex from autode.wrappers.XTB import xtb Config.num_complex_sphere_points = 5 Config.num_complex_random_rotations = 3 # Make a water molecule and optimise at the XTB level water = Molecule(name='water', smiles='O') water.optimise(method=xtb) # Make the NCI complex and find the lowest energy structure trimer = NCIComplex(water, water, water, name='water_trimer') trimer.find_lowest_energy_conformer(lmethod=xtb) trimer.print_xyz_file()
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.get_distance(1, 2),
(1, 3): vaskas.get_distance(1, 3),
(1, 4): vaskas.get_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()