frequency
An introduction to the functions in /BatchOptim/frequency.py
Frequency
(
method: Literal['Coord', 'Grad'] = Coord
block_size: None | int = None
delta: <class 'float'> = 0.01
)
1
2
3
4
5
6
7
8
Calculate normal mode frequency by finite difference method.
Args:
method: 'Coord' for directly calculating 2nd-order deviations, i.e., the Hessian matrix.
'Grad' for calculating 1st-order deviations of grad to get Hessian matrix.
block_size: block size to calculate Hessian. `block_size`*N rows would be input at once.
`None` for input all (3*N)**2 rows coords at once, which requires enough memory.
delta: finite difference delta.
create_hessian
(
func: Callable
coords: <class 'torch.Tensor'>
func_args: Tuple = ()
func_kwargs: Optional[Dict] = None
save_hessian: <class 'bool'> = False
fixed_atom_tensor: torch.Tensor | None = None
)
1
2
3
4
5
6
7
8
9
10
To calculate Hessian matrix in blocks via finite difference
Args:
func:
coords: (N, 3) shape Tensor
func_args: other input arguments of `func`.
func_kwargs: other input keyword arguments of `func`.
save_hessian: whether save calculated hessian as an attribute.
fixed_atom_tensor: the indices of X that fixed, i.e., not performing vibration calculation.
Returns:
hessian_by_autograd
(
grad_func: <class 'inspect._empty'>
X: <class 'torch.Tensor'>
grad_func_args: Tuple = ()
grad_func_kwargs: Optional[Dict] = None
fixed_atom_tensor: torch.Tensor | None = None
save_hessian: <class 'bool'> = False
)
normal_mode
(
func: Callable
coords: <class 'torch.Tensor'>
masses: torch.Tensor | None = None
func_args: Tuple = ()
func_kwargs: Optional[Dict] = None
grad_func: Optional[Callable] = None
grad_func_args: Tuple = ()
grad_func_kwargs: Optional[Dict] = None
fixed_atom_tensor: torch.Tensor | None = None
save_hessian: <class 'bool'> = False
)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
Calculating the normal modes and corresponding frequencies.
Notice: The negative frequencies are actually imaginary frequencies.
Args:
func: potential energy function.
coords: atomic coordinate tensor with shape (N, 3).
masses: atomic masses tensor with shape (N, 3) or (N, ). `None` for tensor filled with 1.
func_args: other input arguments of `func`.
func_kwargs: other input keyword arguments of `func`.
grad_func: gradient function, only used for self.method = 'Grad'.
grad_func_args: other input arguments of `grad_func`.
grad_func_kwargs: other input keyword arguments of `grad_func`.
fixed_atom_tensor: (N, ) or (N, 3), the indices of X that fixed, i.e., not performing vibration calculation.
Only the 1st dimension will be read. Fixing partial degrees of freedom for an atom is not supported.
save_hessian: whether save calculated hessian as an attribute.
Returns:
normal mode frequencies: (3N, ) shape Tensor
normal mode coordinates: (3N, N, 3) shape Tensor