import torch
import numpy as np
import math
from types import SimpleNamespace
from ...utils.algebra_utils import double_factorial
[docs]
def atomic_orbital_norm(basis: SimpleNamespace) -> torch.Tensor:
"""Computes the norm of the atomic orbitals
Args:
basis (Namespace): basis object of the Molecule instance
Returns:
torch.tensor: Norm of the atomic orbitals
Examples::
>>> mol = Molecule('h2.xyz', basis='dzp', calculator='adf')
>>> norm = atomic_orbital_norm(mol.basis)
"""
# spherical
if basis.harmonics_type == "sph":
if basis.radial_type.startswith("sto"):
return norm_slater_spherical(basis.bas_n, basis.bas_exp)
elif basis.radial_type.startswith("gto"):
return norm_gaussian_spherical(basis.bas_n, basis.bas_exp)
else:
raise ValueError("%s is not a valid radial_type")
# cartesian
elif basis.harmonics_type == "cart":
if basis.radial_type.startswith("sto"):
return norm_slater_cartesian(
basis.bas_kx, basis.bas_ky, basis.bas_kz, basis.bas_kr, basis.bas_exp
)
elif basis.radial_type.startswith("gto"):
return norm_gaussian_cartesian(
basis.bas_kx, basis.bas_ky, basis.bas_kz, basis.bas_exp
)
else:
raise ValueError("%s is not a valid radial_type")
[docs]
def norm_slater_spherical(bas_n: torch.Tensor, bas_exp: torch.Tensor) -> torch.Tensor:
"""Normalization of STOs with Spherical Harmonics.
References:
* www.theochem.ru.nl/~pwormer/Knowino/knowino.org/wiki/Slater_orbital
* C Filippi, JCP 105, 213 1996
* Monte Carlo Methods in Ab Initio Quantum Chemistry, B.L. Hammond
Args:
bas_n (torch.Tensor): Principal quantum number
bas_exp (torch.Tensor): Slater exponents
Returns:
torch.Tensor: Normalization factor
"""
nfact = torch.as_tensor(
[math.factorial(2 * n) for n in bas_n], dtype=torch.get_default_dtype()
)
return (2 * bas_exp) ** bas_n * torch.sqrt(2 * bas_exp / nfact)
[docs]
def norm_gaussian_spherical(bas_n: torch.Tensor, bas_exp: torch.Tensor) -> torch.Tensor:
"""Normlization of GTOs with spherical harmonics. \n
* Computational Quantum Chemistry: An interactive Intrduction to basis set theory \n
eq : 1.14 page 23.
Args:
bas_n (torch.tensor): prinicpal quantum number
bas_exp (torch.tensor): slater exponents
Returns:
torch.tensor: normalization factor
"""
bas_n = torch.tensor(bas_n)
bas_n = bas_n + 1.0
exp1 = 0.25 * (2.0 * bas_n + 1.0)
A = torch.tensor(bas_exp) ** exp1
B = 2 ** (2.0 * bas_n + 3.0 / 2)
C = torch.as_tensor(double_factorial(2 * bas_n.int() - 1) * np.pi**0.5).type(
torch.get_default_dtype()
)
return torch.sqrt(B / C) * A
[docs]
def norm_slater_cartesian(
a: torch.Tensor,
b: torch.Tensor,
c: torch.Tensor,
n: torch.Tensor,
exp: torch.Tensor,
) -> torch.Tensor:
"""Normaliation of STos with cartesian harmonics. \n
* Monte Carlo Methods in Ab Initio Quantum Chemistry page 279
Args:
a (torch.tensor): exponent of x
b (torch.tensor): exponent of y
c (torch.tensor): exponent of z
n (torch.tensor): exponent of r
exp (torch.tensor): Sater exponent
Returns:
torch.tensor: normalization factor
"""
lvals = a + b + c + n + 1.0
lfact = torch.as_tensor([math.factorial(int(2 * i)) for i in lvals]).type(
torch.get_default_dtype()
)
prefact = 4 * np.pi * lfact / ((2 * exp) ** (2 * lvals + 1))
num = torch.as_tensor(
double_factorial(2 * a.astype("int") - 1)
* double_factorial(2 * b.astype("int") - 1)
* double_factorial(2 * c.astype("int") - 1)
).type(torch.get_default_dtype())
denom = torch.as_tensor(
double_factorial((2 * a + 2 * b + 2 * c + 1).astype("int"))
).type(torch.get_default_dtype())
return torch.sqrt(1.0 / (prefact * num / denom))
[docs]
def norm_gaussian_cartesian(
a: torch.Tensor, b: torch.Tensor, c: torch.Tensor, exp: torch.Tensor
) -> torch.Tensor:
"""Normaliation of GTOs with cartesian harmonics. \n
* Monte Carlo Methods in Ab Initio Quantum Chemistry page 279
Args:
a (torch.tensor): exponent of x
b (torch.tensor): exponent of y
c (torch.tensor): exponent of z
exp (torch.tensor): Slater exponent
Returns:
torch.tensor: normalization factor
"""
pref = torch.as_tensor((2 * exp / np.pi) ** (0.75))
am1 = (2 * a - 1).astype("int")
x = (4 * exp) ** (a / 2) / torch.sqrt(torch.as_tensor(double_factorial(am1)))
bm1 = (2 * b - 1).astype("int")
y = (4 * exp) ** (b / 2) / torch.sqrt(torch.as_tensor(double_factorial(bm1)))
cm1 = (2 * c - 1).astype("int")
z = (4 * exp) ** (c / 2) / torch.sqrt(torch.as_tensor(double_factorial(cm1)))
return (pref * x * y * z).type(torch.get_default_dtype())