ml4chem package

Subpackages

• ml4chem.atomistic package
  • Subpackages
    • ml4chem.atomistic.features package
      • Submodules
      • ml4chem.atomistic.features.aev module
      • ml4chem.atomistic.features.autoencoders module
      • ml4chem.atomistic.features.base module
      • ml4chem.atomistic.features.cartesian module
      • ml4chem.atomistic.features.coulombmatrix module
      • ml4chem.atomistic.features.cutoff module
      • ml4chem.atomistic.features.gaussian module
      • Module contents
    • ml4chem.atomistic.models package
      • Submodules
      • ml4chem.atomistic.models.autoencoders module
      • ml4chem.atomistic.models.base module
      • ml4chem.atomistic.models.gaussian_process module
      • ml4chem.atomistic.models.kernelridge module
      • ml4chem.atomistic.models.loss module
      • ml4chem.atomistic.models.merger module
      • ml4chem.atomistic.models.neuralnetwork module
      • ml4chem.atomistic.models.se3net module
      • Module contents
  • Submodules
  • ml4chem.atomistic.potentials module
    • Potentials
      • Potentials.autoencoders
      • Potentials.calculate()
      • Potentials.implemented_properties
      • Potentials.load()
      • Potentials.module_names
      • Potentials.save()
      • Potentials.svm_models
      • Potentials.train()
  • Module contents
    • Potentials
      • Potentials.autoencoders
      • Potentials.calculate()
      • Potentials.implemented_properties
      • Potentials.load()
      • Potentials.module_names
      • Potentials.save()
      • Potentials.svm_models
      • Potentials.train()
• ml4chem.backends package
  • Submodules
  • ml4chem.backends.available module
    • available_backends()
    • cuda_is_available()
  • ml4chem.backends.operations module
    • BackendOperations
      • BackendOperations.divide()
      • BackendOperations.dot()
      • BackendOperations.exp()
      • BackendOperations.from_numpy()
      • BackendOperations.log10()
      • BackendOperations.logspace()
      • BackendOperations.norm()
      • BackendOperations.sum()
      • BackendOperations.to_numpy()
  • Module contents
• ml4chem.data package
  • Submodules
  • ml4chem.data.handler module
    • Data
      • Data.get_data()
      • Data.get_total_number_atoms()
      • Data.get_unique_element_symbols()
      • Data.is_valid_structure()
      • Data.prepare_images()
      • Data.to_pandas()
  • ml4chem.data.parser module
    • SinglePointCalculator
      • SinglePointCalculator.get_forces()
      • SinglePointCalculator.get_potential_energy()
    • ani_to_ase()
    • cjson_parser()
    • cjson_to_ase()
    • get_total_energy()
  • ml4chem.data.preprocessing module
    • Preprocessing
      • Preprocessing.fit()
      • Preprocessing.save_to_file()
      • Preprocessing.set()
      • Preprocessing.transform()
  • ml4chem.data.serialization module
    • dump()
    • load()
  • ml4chem.data.utils module
    • ase_to_xyz()
    • split_data()
  • Module contents
• ml4chem.optim package
  • Submodules
  • ml4chem.optim.LBFGS module
    • FullBatchLBFGS
      • FullBatchLBFGS.step()
    • LBFGS
      • LBFGS.curvature_update()
      • LBFGS.line_search()
      • LBFGS.step()
      • LBFGS.two_loop_recursion()
    • is_legal()
    • polyinterp()
  • ml4chem.optim.handler module
    • get_lr()
    • get_lr_scheduler()
    • get_optimizer()
  • Module contents

Submodules

ml4chem.active module

class ml4chem.active.ActiveLearning(labeled, unlabeled, atomistic=True)

Bases: object

Active Learning

Parameters:

• labeled (list) – List of graphs or objects.

• unlabeled (object) – List of graphs or objects.

• atomistic (bool, optional) – Atomistic similarities?, by default False.

run(kernel, max_variance=10, max_iter=None)

Run the ActiveLearning class

Parameters:

• kernel (object) – A kernel to measure similarity.

• max_variance (float, optional) – Maximum variance allowed, by default 10.

• max_iter (int, optional) – Maximum number of iterations allowed, by default None.

ml4chem.metrics module

ml4chem.metrics.compute_mae(outputs, targets, atoms_per_image=None)

Compute MAE

Useful when using futures.

Parameters:

• outputs (list) – List of outputs.

• targets (list) – List if targets.

• atoms_per_image (list) – List of atoms per image.

Returns:

mae – Mean absolute error.

Return type:

float

ml4chem.metrics.compute_mse(outputs, targets, atoms_per_image=None)

Compute MSE

Useful when using futures.

Parameters:

• outputs (list) – List of outputs.

• targets (list) – List if targets.

• atoms_per_image (list) – List of atoms per image.

Returns:

mse – Mean squared error.

Return type:

float

ml4chem.metrics.compute_rmse(outputs, targets, atoms_per_image=None)

Compute RMSE

Useful when using futures.

Parameters:

• outputs (list) – List of outputs.

• targets (list) – List if targets.

• atoms_per_image (list) – List of atoms per image.

Returns:

rmse – Root-mean squared error.

Return type:

float

ml4chem.utils module

ml4chem.utils.convert_elapsed_time(seconds)

Convert elapsed time in seconds to HH:MM:SS format

ml4chem.utils.dynamic_import(name, package, alt_name=None)

A dynamic module importer

Parameters:

• name (str) – Name of the module to be imported.

• package (str) – Path to package. Example: ml4chem.atomistic.features

• alt_name (str) – Alternative module_name.

Returns:

_class – An class object.

Return type:

obj

ml4chem.utils.get_chunks(sequence, chunk_size, svm=True)

A function that yields a list in chunks

Parameters:

• sequence (list or dictionary) – A list or a dictionary to be split.

• chunk_size (int) – Number of elements in each group.

• svm (bool) – Whether or not these chunks are going to be used for kernel methods.

ml4chem.utils.get_hash(image)

Get the SHA1 hash of an image object

Parameters:

image (object) – An image to be hashed.

Returns:

_hash – Hash of image in string format

Return type:

str

ml4chem.utils.get_header_message()

Function that returns ML4Chem header

ml4chem.utils.get_neighborlist(image, cutoff)

Get the list of neighbors

Parameters:

image (object) – ASE image.

Return type:

A list of neighbors with offset distances.

ml4chem.utils.get_number_of_parameters(model)

Get the number of parameters

Parameters:

model (obj) – Pytorch model to perform forward() and get gradients.

Returns:

• (total_params, train_params) tuple with total number of parameters and

• number of trainable parameters.

ml4chem.utils.lod_to_list(data, svm=False, requires_grad=False)

List Of Dict (lod) to list

Parameters:

• data (list) – A list with ml4chem dictionaries. Those ones coming from get_chunks()

• svm (bool, optional.) – Whether or not these chunks are going to be used for kernel methods, by default False.

• requires_grad (bool, optional.) – Do we require gradients?, by default False.

Returns:

A list of tensors or list of float.

Return type:

_list

ml4chem.utils.logger(filename=None, level=None, format=None, filemode='a')

A wrapper to the logging python module

This module is useful for cases where we need to log in a for loop different files. It also will allow more flexibility later on how the logging format could evolve.

Parameters:

• filename (str, optional) – Name of logfile. If no filename is provided, we output to stdout.

• level (str, optional) – Level of logging messages, by default ‘info’. Supported are: ‘info’ and ‘debug’.

• format (str, optional) – Format of logging messages, by default ‘%(message)s’.

• filemode (str, optional) – If filename is specified, open the file in this mode. Defaults to “a”. Supported modes are: “r” (read), “w” (write), “a” (append).

Returns:

A logger object.

Return type:

logger

ml4chem.visualization module

Module contents