BatchMD
An introduction to the functions in /BatchMD
NVE
(
time_step: <class 'float'>
max_step: <class 'int'>
T_init: <class 'float'> = 298.15
output_file: str | None = None
output_structures_per_step: <class 'int'> = 1
device: str | torch.device = cpu
verbose: <class 'int'> = 2
)
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Micro canonical ensemble (NVE) molecular dynamics.
Parameters:
time_step: float, time per step (ps).
max_step: int, maximum steps.
T_init: float, initial temperature, only to generate initial velocities of atoms by Maxwell-Boltzmann distribution. If V_init is given, T_init will be ignored.
output_structures_per_step: int, output structures per output_structures_per_step steps.
device: str|torch.device, device that program rum on.
verbose: int, control the detailed degree of output information. 0 for silence, 1 for output Energy and Forces per step, 2 for output all structures.
Methods:
run: run BatchMD.
run
(
func: Any | torch.nn.modules.module.Module
X: <class 'torch.Tensor'>
Element_list: Union[List[List[str]], List[List[int]]]
V_init: torch.Tensor | None = None
grad_func: Any | torch.nn.modules.module.Module = None
func_args: Sequence = ()
func_kwargs: Optional[Dict] = None
grad_func_args: Sequence = ()
grad_func_kwargs: Optional[Dict] = None
is_grad_func_contain_y: <class 'bool'> = True
batch_indices: Union[List[int], Tuple[int, ...], torch.Tensor, numpy.ndarray, NoneType] = None
fixed_atom_tensor: Optional[torch.Tensor] = None
is_fix_mass_center: <class 'bool'> = False
)
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Parameters:
func: the main function of instantiated torch.nn.Module class.
X: Tensor[n_batch, n_atom, 3], the atom coordinates that input to func. If a 2D X was given, the first dimension would be set to 1.
Element_list: List[List[str | int]], the atomic type (element) corresponding to each row of each batch in X.
V_init: the initial velocities of each atom. If None, a random velocity generated by Boltzmann distribution would be set.
grad_func: user-defined function that grad_func(X, ...) returns the func's gradient at X. if None, grad_func(X, ...) = th.autograd.grad(func(X, ...), X).
func_args: optional, other input of func.
func_kwargs: optional, other input of func.
grad_func_args: optional, other input of grad_func.
grad_func_kwargs: optional, other input of grad_func.
is_grad_func_contain_y: bool, if True, grad_func contains output of func followed by X i.e., grad = grad_func(X, y, ...), else grad = grad_func(X, ...)
batch_indices: the split points for given X, Element_list & V_init, must be 1D integer array_like.
the format of batch_indices is the same as `split_size_or_sections` in torch.split:
batch_indices = (n1, n2, ..., nN) will split X, Element_list & V_init into N parts, and ith parts has ni atoms. sum(n1, ..., nN) = X.shape[1]
fixed_atom_tensor: the indices of X that fixed.
is_fix_mass_center: whether transition coordinates and velocities of atoms into the mass center.
Returns:
min func: Tensor(n_batch, ), the minimum of func.
argmin func: Tensor(X.shape), the X corresponds to min func.
NVT
(
time_step: <class 'float'>
max_step: <class 'int'>
thermostat: Literal['Langevin', 'VR', 'Nose-Hoover', 'CSVR']
thermostat_config: Optional[Dict] = None
T_init: <class 'float'> = 298.15
output_file: str | None = None
output_structures_per_step: <class 'int'> = 1
device: str | torch.device = cpu
verbose: <class 'int'> = 2
)
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Canonical ensemble (NVT) molecular dynamics.
Parameters:
time_step: float, time per step (ps).
max_step: maxmimum steps.
thermostat: str, the thermostat of NVT ensemble.
thermostat_config: Dict|None, configs of thermostat. {'damping_coeff': float} for Langevin, {'time_const': float} for CSVR, {'virt_mass': float} for Nose-Hoover.
T_init: initial temperature, only to generate initial velocities of atoms by Maxwell-Boltzmann distribution. If V_init is given, T_init will be ignored.
output_structures_per_step: int, output structures per output_structures_per_step steps.
device: device that the program rum on.
verbose: control the detailed degree of output information. 0 for silence, 1 for output Energy and Forces per step, 2 for output all structures.
Methods:
run: run the NVT ensemble BatchMD.
run
(
func: Any | torch.nn.modules.module.Module
X: <class 'torch.Tensor'>
Element_list: Union[List[List[str]], List[List[int]]]
V_init: torch.Tensor | None = None
grad_func: Any | torch.nn.modules.module.Module = None
func_args: Sequence = ()
func_kwargs: Optional[Dict] = None
grad_func_args: Sequence = ()
grad_func_kwargs: Optional[Dict] = None
is_grad_func_contain_y: <class 'bool'> = True
batch_indices: Union[List[int], Tuple[int, ...], torch.Tensor, numpy.ndarray, NoneType] = None
fixed_atom_tensor: Optional[torch.Tensor] = None
is_fix_mass_center: <class 'bool'> = False
)
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Parameters:
func: the main function of instantiated torch.nn.Module class.
X: Tensor[n_batch, n_atom, 3], the atom coordinates that input to func. If a 2D X was given, the first dimension would be set to 1.
Element_list: List[List[str | int]], the atomic type (element) corresponding to each row of each batch in X.
V_init: the initial velocities of each atom. If None, a random velocity generated by Boltzmann distribution would be set.
grad_func: user-defined function that grad_func(X, ...) returns the func's gradient at X. if None, grad_func(X, ...) = th.autograd.grad(func(X, ...), X).
func_args: optional, other input of func.
func_kwargs: optional, other input of func.
grad_func_args: optional, other input of grad_func.
grad_func_kwargs: optional, other input of grad_func.
is_grad_func_contain_y: bool, if True, grad_func contains output of func followed by X i.e., grad = grad_func(X, y, ...), else grad = grad_func(X, ...)
batch_indices: the split points for given X, Element_list & V_init, must be 1D integer array_like.
the format of batch_indices is the same as `split_size_or_sections` in torch.split:
batch_indices = (n1, n2, ..., nN) will split X, Element_list & V_init into N parts, and ith parts has ni atoms. sum(n1, ..., nN) = X.shape[1]
fixed_atom_tensor: the indices of X that fixed.
is_fix_mass_center: whether transition coordinates and velocities of atoms into the mass center.
Returns:
min func: Tensor(n_batch, ), the minimum of func.
argmin func: Tensor(X.shape), the X corresponds to min func.
BiasedMD
(
time_step: <class 'float'>
max_step: <class 'int'>
thermostat: Literal['Langevin', 'VR', 'Nose-Hoover', 'CSVR']
thermostat_config: Optional[Dict] = None
T_init: <class 'float'> = 298.15
output_file: str | None = None
output_structures_per_step: <class 'int'> = 1
device: str | torch.device = cpu
verbose: <class 'int'> = 2
)
1
Performing MD with given external-biased potentials.
run
(
func: Any | torch.nn.modules.module.Module
X: <class 'torch.Tensor'>
Element_list: Union[List[List[str]], List[List[int]]]
V_init: torch.Tensor | None = None
grad_func: Any | torch.nn.modules.module.Module = None
func_args: Union[Tuple, List] = ()
func_kwargs: Optional[Dict] = None
grad_func_args: Union[Tuple, List] = ()
grad_func_kwargs: Optional[Dict] = None
is_grad_func_contain_y: <class 'bool'> = True
batch_indices: Union[List[int], Tuple[int, ...], torch.Tensor, numpy.ndarray, NoneType] = None
fixed_atom_tensor: Optional[torch.Tensor] = None
is_fix_mass_center: <class 'bool'> = False
)
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Args:
func:
X:
Element_list:
V_init:
grad_func:
func_args:
func_kwargs:
grad_func_args:
grad_func_kwargs:
is_grad_func_contain_y:
batch_indices:
fixed_atom_tensor:
is_fix_mass_center:
Returns:
set_external_potential
(
potential_func: Callable
potential_grad_func: Optional[Callable] = None
func_args: Tuple = ()
func_kwargs: Optional[Dict] = None
grad_func_args: Tuple = ()
grad_func_kwargs: Optional[Dict] = None
is_grad_contain_y: <class 'bool'> = True
)
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Set the external potential function.
Args:
potential_func: the function of external potential
potential_grad_func: the gradient of external potential. if None, torch.autograd is applied.
func_args: arguments of `potential_func`
func_kwargs: key words arguments of `potential_func`
grad_func_args: arguments of `potential_grad_func`
grad_func_kwargs: key words arguments of `potential_grad_func`
is_grad_contain_y: whether `potential_grad_func` needs dependent variable y as input. if True, potential_grad_func = g(X, y), else g(X)
#is_dynamic: whether potential function containing time as independent variables. if True, potential_func = f(X, t), else f(X).
Returns: