def make_struc():
slab_ideal=fcc110('Au',size=(4,2,8), a=4.163737636, vacuum=20)
#slab_ideal=fcc110('Au',size=(4,8,i), vacuum=20)
write('slab_ideal.struct',slab_ideal)
slab_ideal.edit()
#natoms_100=(len(slab_100.numbers))
#print ('positions',slab_ideal.positions)
natoms_ideal=len(slab_ideal.positions)
#print(natoms_ideal)
struc_ideal=Struc(ase2struc(slab_ideal))
return natoms_ideal,struc_ideal
def test_analyse_comparing_bond_lengths():
import numpy as np
from ase.build import fcc111, fcc110
from carmm.analyse.bonds import comparing_bonds_lengths
# Build some models
atoms1 = fcc111('Pd', (2, 2, 1), vacuum=20.0)
atoms2 = fcc110('Pd', (2, 2, 1), vacuum=20.0)
difference = comparing_bonds_lengths(atoms1, atoms2)
# Assertion test
assert (1e-5 > difference[np.argmax(difference)] - 2.013612)
def generate_data():
"""Generates atomic images for tests."""
atoms = fcc110('Pt', (2, 2, 1), vacuum=7.)
atoms[0].symbol = 'Cu'
del atoms[3]
atoms.set_calculator(EMT())
atoms.get_potential_energy()
atoms.get_forces()
newatoms = atoms.copy()
newatoms.set_calculator(EMT())
newatoms[0].position += (0.27, -0.11, 0.3)
newatoms[1].position += (0.12, 0.03, -0.22)
newatoms.get_potential_energy()
newatoms.get_forces()
return [atoms, newatoms]
def generate_data(count, filename='training.traj'):
"""Generates test or training data with a simple MD simulation."""
if os.path.exists(filename):
return
traj = ase.io.Trajectory(filename, 'w')
atoms = fcc110('Pt', (2, 2, 2), vacuum=7.)
atoms.extend(Atoms([Atom('Cu', atoms[7].position + (0., 0., 2.5)),
Atom('Cu', atoms[7].position + (0., 0., 5.))]))
atoms.set_constraint(FixAtoms(indices=[0, 2]))
atoms.set_calculator(EMT())
atoms.get_potential_energy()
traj.write(atoms)
MaxwellBoltzmannDistribution(atoms, 300. * units.kB)
dyn = VelocityVerlet(atoms, dt=1. * units.fs)
for step in range(count - 1):
dyn.run(50)
traj.write(atoms)
def make_struc(i):
slab_ideal = fcc110('Au', size=(16, 16, 40), a=4.0800000183054, vacuum=i)
#slab_ideal=fcc110('Au',size=(4,8,i), vacuum=20)
write('slab_ideal.struct', slab_ideal)
#slab_ideal.edit()
#natoms_100=(len(slab_100.numbers))
list_z = [
slab_ideal.positions[i][2] for i in range(len(slab_ideal.positions))
]
min_z = min(list_z)
list_x_top_row = []
for i in range(len(slab_ideal.positions)):
if slab_ideal.positions[i][2] == min_z:
list_x_top_row.append(slab_ideal.positions[i][0])
list_x_top_row_concise = list(set(list_x_top_row))
list_x_top_row_concise.sort()
select_list_x = list_x_top_row_concise[
1::2] #this slices list_x_top_row_concise using even index
atoms_to_delete = []
atoms_to_delete_index = []
for i in range(len(slab_ideal.positions)):
if slab_ideal.positions[i][
0] in select_list_x and slab_ideal.positions[i][2] == min_z:
atoms_to_delete.append(slab_ideal.positions[i])
atoms_to_delete_index.append(i)
atoms_to_delete_index.sort()
atoms_to_delete_index.reverse()
#to delete atoms in slab to create the 1*2 missing row reconstruction
for i in atoms_to_delete_index:
slab_ideal.pop(i)
#slab_ideal.edit()
#print('list of atoms is',slab_ideal.positions)
#print('list_x_top_row',list_x_top_row)
#print('list_x_top_row_concise',list_x_top_row_concise)
#print ('min z is',min_z)
#print('select_list_x',select_list_x)
#print('atoms_to_delete',atoms_to_delete)
print('atoms_to_delete_index', atoms_to_delete_index)
natoms_ideal = len(slab_ideal.positions)
print('number of atoms left', natoms_ideal)
struc_ideal = Struc(ase2struc(slab_ideal))
return natoms_ideal, struc_ideal
def generate_data(count):
"""Generates test or training data with a simple MD simulation."""
atoms = fcc110('Pt', (2, 2, 2), vacuum=7.)
adsorbate = Atoms([Atom('Cu', atoms[7].position + (0., 0., 2.5)),
Atom('Cu', atoms[7].position + (0., 0., 5.))])
atoms.extend(adsorbate)
atoms.set_constraint(FixAtoms(indices=[0, 2]))
atoms.set_calculator(EMT())
MaxwellBoltzmannDistribution(atoms, 300. * units.kB)
dyn = VelocityVerlet(atoms, dt=1. * units.fs)
newatoms = atoms.copy()
newatoms.set_calculator(EMT())
newatoms.get_potential_energy()
images = [newatoms]
for step in range(count - 1):
dyn.run(50)
newatoms = atoms.copy()
newatoms.set_calculator(EMT())
newatoms.get_potential_energy()
images.append(newatoms)
return images
"""
根据 ASE tutorial Constrained minima hopping (global optimization)修改而来
"""
from ase import Atoms, Atom
from ase.build import fcc110
from ase.optimize import BFGS
from ase.calculators.emt import EMT
from ase.constraints import FixAtoms
from scan_ts.constraints import FixAtomsCom, FixAtomsComXY
import numpy as np
# Make the Pt 110 slab.
atoms = fcc110('Pt', (2, 2, 2), vacuum=7., periodic=True)
# Add the Cu2 adsorbate.
adsorbate = Atoms([
Atom('Cu', atoms[7].position + (0., 0., 2.5)),
Atom('Cu', atoms[7].position + (0.5, 0.5, 4.5))
])
atoms.extend(adsorbate)
atoms.write('init.cif')
# 输出初始结构 COM
index = [atom.index for atom in atoms if atom.symbol == 'Cu']
masses = atoms.get_masses()[index]
com_init = np.dot(masses, atoms.positions[index]) / masses.sum()
# Constrain the surface to be fixed and a FixAtomsCom or FixAtomsComXY constraint between
# the adsorbate atoms.
constraints = [
FixAtoms(indices=[atom.index for atom in atoms if atom.symbol == 'Pt']),
def test_hookean():
"""
Test of Hookean constraint.
Checks for activity in keeping a bond, preventing vaporization, and
that energy is conserved in NVE dynamics.
"""
import numpy as np
from ase import Atoms, Atom
from ase.build import fcc110
from ase.calculators.emt import EMT
from ase.constraints import FixAtoms, Hookean
from ase.md import VelocityVerlet
from ase import units
class SaveEnergy:
"""Class to save energy."""
def __init__(self, atoms):
self.atoms = atoms
self.energies = []
def __call__(self):
self.energies.append(atoms.get_total_energy())
# Make Pt 110 slab with Cu2 adsorbate.
atoms = fcc110('Pt', (2, 2, 2), vacuum=7.)
adsorbate = Atoms([
Atom('Cu', atoms[7].position + (0., 0., 2.5)),
Atom('Cu', atoms[7].position + (0., 0., 5.0))
])
atoms.extend(adsorbate)
calc = EMT()
atoms.calc = calc
# Constrain the surface to be fixed and a Hookean constraint between
# the adsorbate atoms.
constraints = [
FixAtoms(indices=[atom.index for atom in atoms
if atom.symbol == 'Pt']),
Hookean(a1=8, a2=9, rt=2.6, k=15.),
Hookean(a1=8, a2=(0., 0., 1., -15.), k=15.)
]
atoms.set_constraint(constraints)
# Give it some kinetic energy.
momenta = atoms.get_momenta()
momenta[9, 2] += 20.
momenta[9, 1] += 2.
atoms.set_momenta(momenta)
# Propagate in Velocity Verlet (NVE).
dyn = VelocityVerlet(atoms, timestep=1.0 * units.fs)
energies = SaveEnergy(atoms)
dyn.attach(energies)
dyn.run(steps=100)
# Test the max bond length and position.
bondlength = np.linalg.norm(atoms[8].position - atoms[9].position)
assert bondlength < 3.0
assert atoms[9].z < 15.0
# Test that energy was conserved.
assert max(energies.energies) - min(energies.energies) < 0.01
# Make sure that index shuffle works.
neworder = list(range(len(atoms)))
neworder[8] = 9 # Swap two atoms.
neworder[9] = 8
atoms = atoms[neworder]
assert atoms.constraints[1].indices[0] == 9
assert atoms.constraints[1].indices[1] == 8
assert atoms.constraints[2].index == 9
from ase import units
class SaveEnergy:
"""Class to save energy."""
def __init__(self, atoms):
self.atoms = atoms
self.energies = []
def __call__(self):
self.energies.append(atoms.get_total_energy())
# Make Pt 110 slab with Cu2 adsorbate.
atoms = fcc110('Pt', (2, 2, 2), vacuum=7.)
adsorbate = Atoms([Atom('Cu', atoms[7].position + (0., 0., 2.5)),
Atom('Cu', atoms[7].position + (0., 0., 5.0))])
atoms.extend(adsorbate)
calc = EMT()
atoms.set_calculator(calc)
# Constrain the surface to be fixed and a Hookean constraint between
# the adsorbate atoms.
constraints = [FixAtoms(indices=[atom.index for atom in atoms if
atom.symbol == 'Pt']),
Hookean(a1=8, a2=9, rt=2.6, k=15.),
Hookean(a1=8, a2=(0., 0., 1., -15.), k=15.)]
atoms.set_constraint(constraints)
# Give it some kinetic energy.
from ase import units
class SaveEnergy:
"""Class to save energy."""
def __init__(self, atoms):
self.atoms = atoms
self.energies = []
def __call__(self):
self.energies.append(atoms.get_total_energy())
# Make Pt 110 slab with Cu2 adsorbate.
atoms = fcc110('Pt', (2, 2, 2), vacuum=7.)
adsorbate = Atoms([Atom('Cu', atoms[7].position + (0., 0., 2.5)),
Atom('Cu', atoms[7].position + (0., 0., 5.0))])
atoms.extend(adsorbate)
calc = EMT()
atoms.set_calculator(calc)
# Constrain the surface to be fixed and a Hookean constraint between
# the adsorbate atoms.
constraints = [FixAtoms(indices=[atom.index for atom in atoms if
atom.symbol == 'Pt']),
Hookean(a1=8, a2=9, rt=2.6, k=15.),
Hookean(a1=8, a2=(0., 0., 1., -15.), k=15.)]
atoms.set_constraint(constraints)
# Give it some kinetic energy.
def get_example_slab(adsorbate=False, type="CO2", surface="111"):
'''
Example model generation to show tool functionality
Parameters:
adsorbate: Boolean
Whether or not to include an adsorbate CO2 on the surface
type: str
Two adsorbate types - "CO2" and "2Cu". CO2 behaviour during optimisation
is unrealistic in EMT.
surface: string
The surface facet to return
'''
from math import sqrt
from ase.build import molecule, add_adsorbate, fcc111
#### Traditional ASE functionality #####
element = 'Au'
lattice_parameter = 2.939
width = 3
depth = 2
vacuum = 10.0
# Create surface
if surface == "111":
from ase.build import fcc111
slab = fcc111(element,
a=lattice_parameter * sqrt(2),
size=(width, width, depth),
vacuum=vacuum)
elif surface == "100":
from ase.build import fcc100
slab = fcc100(element,
a=lattice_parameter * sqrt(2),
size=(width, width, depth),
vacuum=vacuum)
elif surface == "110":
from ase.build import fcc110
slab = fcc110(element,
a=lattice_parameter * sqrt(2),
size=(width, width, depth),
vacuum=vacuum)
else:
raise NotImplementedError("The surface facet requested (" + surface +
") is not available")
# Put adsorbate on the surface
if adsorbate:
if type == "CO2":
position = (slab[17].position[0], slab[17].position[1])
elif type == "2Cu":
position = (slab[5].x, slab[5].y)
species = get_example_adsorbate(type)
add_adsorbate(slab, species, 3.0, position=position)
# Make the model a bit more technically complete - include a calculator.
from ase.calculators.emt import EMT
slab.calc = EMT()
return slab
