Models#

torch::jit::Module metatensor_torch::load_atomistic_model(std::string path, c10::optional<c10::Device> device = c10::nullopt)#: Check and then load the metatensor atomistic model at the given path.

void metatensor_torch::check_atomistic_model(std::string path)#: Check the exported metatensor atomistic model at the given path, and warn/error as required.

using metatensor_torch::ModelOutput = torch::intrusive_ptr<ModelOutputHolder>#: TorchScript will always manipulate ModelOutputHolder through a torch::intrusive_ptr

class ModelOutputHolder : public CustomClassHolder#

Description of one of the quantity a model can compute.

Public Functions

inline ModelOutputHolder(std::string quantity_, std::string unit_, bool per_atom_, std::vector<std::string> explicit_gradients_)#: Initialize ModelOutput with the given data.

std::string to_json() const#: Serialize a ModelOutput to a JSON string.

Public Members

std::string quantity#: quantity of the output (e.g. energy, dipole, …). If this is an empty string, no unit conversion will be performed.

std::string unit#: unit of the output. If this is an empty string, no unit conversion will be performed.

bool per_atom = false#: is the output defined per-atom or for the overall structure

std::vector<std::string> explicit_gradients#: Which gradients should be computed eagerly and stored inside the output TensorMap

Public Static Functions

static ModelOutput from_json(const std::string &json)#: Load a serialized ModelOutput from a JSON string.

using metatensor_torch::ModelCapabilities = torch::intrusive_ptr<ModelCapabilitiesHolder>#: TorchScript will always manipulate ModelCapabilitiesHolder through a torch::intrusive_ptr

class ModelCapabilitiesHolder : public CustomClassHolder#

Description of a model capabilities, i.e. everything a model can do.

Public Functions

inline ModelCapabilitiesHolder(std::string length_unit_, std::vector<int64_t> species_, torch::Dict<std::string, ModelOutput> outputs_)#: Initialize ModelCapabilities with the given data.

std::string to_json() const#: Serialize a ModelCapabilities to a JSON string.

Public Members

std::string length_unit#: unit of lengths the model expects as input

std::vector<int64_t> species#: which atomic species the model can handle

torch::Dict<std::string, ModelOutput> outputs#: all possible outputs from this model and corresponding settings

Public Static Functions

static ModelCapabilities from_json(const std::string &json)#: Load a serialized ModelCapabilities from a JSON string.

using metatensor_torch::ModelEvaluationOptions = torch::intrusive_ptr<ModelEvaluationOptionsHolder>#: TorchScript will always manipulate ModelEvaluationOptionsHolder through a torch::intrusive_ptr

class ModelEvaluationOptionsHolder : public CustomClassHolder#

Options requested by the simulation engine when running with a model.

Public Functions

ModelEvaluationOptionsHolder(std::string length_unit, torch::Dict<std::string, ModelOutput> outputs, torch::optional<TorchLabels> selected_atoms)#: Initialize ModelEvaluationOptions with the given data.

inline torch::optional<TorchLabels> get_selected_atoms() const#: Only run the calculation for a selected subset of atoms. If this is set to None, run the calculation on all atoms. If this is a set of Labels, it will have two dimensions named "system" and "atom", containing the 0-based indices of all the atoms in the selected subset.

void set_selected_atoms(torch::optional<TorchLabels> selected_atoms)#: Setter for selected_atoms

std::string to_json() const#: Serialize a ModelEvaluationOptions to a JSON string.

Public Members

std::string length_unit#: unit of lengths the engine uses for the model input

torch::Dict<std::string, ModelOutput> outputs#: requested outputs for this run and corresponding settings

Public Static Functions

static ModelEvaluationOptions from_json(const std::string &json)#: Load a serialized ModelEvaluationOptions from a JSON string.