tfep.potentials.gromacs.GROMACSPotential

class tfep.potentials.gromacs.GROMACSPotential(tpr_file_path: str, launcher: Launcher | None = None, positions_unit: Unit | None = None, energy_unit: Unit | None = None, precompute_gradient: bool = True, working_dir_path: str | List[str] | None = None, cleanup_working_dir: bool = False, parallelization_strategy: ParallelizationStrategy | None = None, launcher_kwargs: Dict[str, Any] | None = None, mdrun_kwargs: Dict[str, Any] | None = None, on_mdrun_error: Literal['raise', 'nan'] = 'raise')

Bases: PotentialBase

Potential energy and forces with GROMACS.

This Module wraps :class:.GROMACSPotentialEnergyFunc to provide a differentiable potential energy function for training.

__init__(tpr_file_path: str, launcher: Launcher | None = None, positions_unit: Unit | None = None, energy_unit: Unit | None = None, precompute_gradient: bool = True, working_dir_path: str | List[str] | None = None, cleanup_working_dir: bool = False, parallelization_strategy: ParallelizationStrategy | None = None, launcher_kwargs: Dict[str, Any] | None = None, mdrun_kwargs: Dict[str, Any] | None = None, on_mdrun_error: Literal['raise', 'nan'] = 'raise')

Constructor.

Parameters:

• tpr_file_path (str) – The path to the .tpr file holding the information on topology and the simulation parameters. The coordinates in this file are not important as they will be overwritten by the positions passed in the forward pass.

• launcher (tfep.utils.cli.Launcher, optional) – The Launcher to use to run the mdrun command used to compute energies and forces. If not passed, a new tfep.utils.cli.Launcher is created.

• positions_unit (pint.Unit, optional) – The unit of the positions passed. This is used to appropriately convert batch_positions to GROMACS’ units. If None, no conversion is performed, which assumes that the input positions are in nm.

• energy_unit (pint.Unit, optional) – The unit used for the returned energies (and as a consequence forces). This is used to appropriately convert GROMACS energies into the desired units. If None, no conversion is performed, which means that energies and forces will be in kJ/mol and kJ/mol/nm respectively.

• precompute_gradient (bool, optional) – If False, the forces are not read after executing GROMACS. This might save a small amount of time if backpropagation is not needed.

• working_dir_path (str or List[str], optional) – The working directory to be used to run the GROMACS’ commands. This must exist. If a list, batch_positions[i] is evaluated in the directory working_dir_path[i].

• cleanup_working_dir (bool, optional) – If True and working_dir_path is passed, all the files inside the working directory are removed after executing GROMACS. The directory(s) itself is not deleted.

• parallelization_strategy (tfep.utils.parallel.ParallelizationStrategy, optional) – The parallelization strategy used to distribute batches of energy and gradient calculations. By default, these are executed serially.

• launcher_kwargs (Dict, optional) – Other kwargs for launcher (with the exception of cwd which is automatically determined based on working_dir_path).

• mdrun_kwargs (Dict, optional) – Other kwargs for GmxMdrun.

• on_mdrun_error (Literal[‘raise’, ‘nan’], optional) – Whether to raise an exception or return NaN potential when the single- point energy calculation with mdrun fails. In the latter case, the returned forces are set to zero.

See also

GROMACSPotentialEnergyFunc

More details on input parameters and implementation details.

Methods

__init__(tpr_file_path[, launcher, ...])	Constructor.
add_module(name, module)	Add a child module to the current module.
apply(fn)	Apply fn recursively to every submodule (as returned by .children()) as well as self.
bfloat16()	Casts all floating point parameters and buffers to bfloat16 datatype.
buffers([recurse])	Return an iterator over module buffers.
children()	Return an iterator over immediate children modules.
compile(*args, **kwargs)	Compile this Module's forward using torch.compile().
cpu()	Move all model parameters and buffers to the CPU.
cuda([device])	Move all model parameters and buffers to the GPU.
default_energy_unit(unit_registry)	Return the default energy units.
default_positions_unit(unit_registry)	Return the default positions units.
double()	Casts all floating point parameters and buffers to double datatype.
eval()	Set the module in evaluation mode.
extra_repr()	Set the extra representation of the module.
float()	Casts all floating point parameters and buffers to float datatype.
forward(batch_positions, batch_cell)	Compute a differential potential energy for a batch of configurations.
get_buffer(target)	Return the buffer given by target if it exists, otherwise throw an error.
get_extra_state()	Return any extra state to include in the module's state_dict.
get_parameter(target)	Return the parameter given by target if it exists, otherwise throw an error.
get_submodule(target)	Return the submodule given by target if it exists, otherwise throw an error.
half()	Casts all floating point parameters and buffers to half datatype.
ipu([device])	Move all model parameters and buffers to the IPU.
load_state_dict(state_dict[, strict, assign])	Copy parameters and buffers from state_dict into this module and its descendants.
modules()	Return an iterator over all modules in the network.
mtia([device])	Move all model parameters and buffers to the MTIA.
named_buffers([prefix, recurse, ...])	Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.
named_children()	Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.
named_modules([memo, prefix, remove_duplicate])	Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.
named_parameters([prefix, recurse, ...])	Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.
parameters([recurse])	Return an iterator over module parameters.
register_backward_hook(hook)	Register a backward hook on the module.
register_buffer(name, tensor[, persistent])	Add a buffer to the module.
register_forward_hook(hook, *[, prepend, ...])	Register a forward hook on the module.
register_forward_pre_hook(hook, *[, ...])	Register a forward pre-hook on the module.
register_full_backward_hook(hook[, prepend])	Register a backward hook on the module.
register_full_backward_pre_hook(hook[, prepend])	Register a backward pre-hook on the module.
register_load_state_dict_post_hook(hook)	Register a post-hook to be run after module's load_state_dict() is called.
register_load_state_dict_pre_hook(hook)	Register a pre-hook to be run before module's load_state_dict() is called.
register_module(name, module)	Alias for add_module().
register_parameter(name, param)	Add a parameter to the module.
register_state_dict_post_hook(hook)	Register a post-hook for the state_dict() method.
register_state_dict_pre_hook(hook)	Register a pre-hook for the state_dict() method.
requires_grad_([requires_grad])	Change if autograd should record operations on parameters in this module.
set_extra_state(state)	Set extra state contained in the loaded state_dict.
set_submodule(target, module)	Set the submodule given by target if it exists, otherwise throw an error.
share_memory()	See torch.Tensor.share_memory_().
state_dict(*args[, destination, prefix, ...])	Return a dictionary containing references to the whole state of the module.
to(*args, **kwargs)	Move and/or cast the parameters and buffers.
to_empty(*, device[, recurse])	Move the parameters and buffers to the specified device without copying storage.
train([mode])	Set the module in training mode.
type(dst_type)	Casts all parameters and buffers to dst_type.
xpu([device])	Move all model parameters and buffers to the XPU.
zero_grad([set_to_none])	Reset gradients of all model parameters.

Attributes

DEFAULT_ENERGY_UNIT	The default energy unit.
DEFAULT_POSITIONS_UNIT	The default positions unit.
T_destination
call_super_init
dump_patches
energy_unit	The energy units of the returned potential.
positions_unit	The positions unit requested for the input.
training

DEFAULT_ENERGY_UNIT: str = 'kJ/mol'

The default energy unit.

DEFAULT_POSITIONS_UNIT: str = 'nanometer'

The default positions unit.

classmethod default_energy_unit(unit_registry) → Quantity

Return the default energy units.

classmethod default_positions_unit(unit_registry) → Quantity

Return the default positions units.

property energy_unit: Quantity

The energy units of the returned potential.

forward(batch_positions: Tensor, batch_cell: Tensor) → Tensor

Compute a differential potential energy for a batch of configurations.

Parameters:

• batch_positions (torch.Tensor) – A tensor of positions in flattened format (i.e., with shape (batch_size, 3*n_atoms)) in units of self.positions_unit.

• batch_cell (torch.Tensor) – Shape (batch_size, 6). Unitcell dimensions. For each data point, the first 3 elements represent the vector lengths in units of self.positions_unit and the last 3 their respective angles (in degrees).

Returns:

potential_energy – potential_energy[i] is the potential energy of configuration batch_positions[i] and batch_cell[i] in units of self.energy_unit (or GROMACS units if energy_unit is not provided).

Return type:

torch.Tensor

property positions_unit: Quantity

The positions unit requested for the input.