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.calculation import Calculation
from autode.methods import ORCA
import matplotlib.pyplot as plt
import numpy as np
# Initialise the electronic structure method and a list of different
# single point energy keywords
orca = ORCA()
keywords_list = {
'PBE': SinglePointKeywords(['PBE', 'def2-SVP']),
'PBE0': SinglePointKeywords(['PBE0', 'def2-SVP']),
'B3LYP': SinglePointKeywords(['B3LYP', 'def2-SVP'])
}
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)
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()
from autode import Molecule
from autode.pes.pes_1d import PES1d
from autode.methods import XTB
import numpy as np
# Initialise the electronic structure method (XTB) and water molecule
xtb = XTB()
water = Molecule(name='H2O', smiles='O')
# Initialise a potential energy surface where the reactant and product is
# the same. A product is required to call pes.products_made() to check products
# are generated somewhere on the surface via graph isomorphism
pes = PES1d(reactant=water,
product=water,
rs=np.linspace(0.65, 2.0, num=20),
r_idxs=(0, 1))
# Calculate the potential energy surface at the XTB level optimising
# all degrees of freedom other than the O-H distance
pes.calculate(name='OH_PES', method=xtb, keywords=xtb.keywords.low_opt)
# Print the r values and energies
for i in range(pes.n_points):
print(f'{pes.rs[i, 0]:.3f} {pes.species[i].energy:.4f}')
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()