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module data.dataset


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function collate_graphs

collate_graphs(batch_data: 'list')

Collate of list of (graph, target) into batch data.

Args:

Returns:


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function get_train_val_test_loader

get_train_val_test_loader(
    dataset: 'Dataset',
    batch_size: 'int' = 64,
    train_ratio: 'float' = 0.8,
    val_ratio: 'float' = 0.1,
    return_test: 'bool' = True,
    num_workers: 'int' = 0,
    pin_memory: 'bool' = True
)

Randomly partition a dataset into train, val, test loaders.

Args:

Returns: train_loader, val_loader and optionally test_loader


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function get_loader

get_loader(dataset, batch_size=64, num_workers=0, pin_memory=True)

Get a dataloader from a dataset.

Args:

Returns: data_loader


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class StructureData

A simple torch Dataset of structures.

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method __init__

__init__(
    structures: 'list[Structure]',
    energies: 'list[float]',
    forces: 'list[Sequence[Sequence[float]]]',
    stresses: 'list[Sequence[Sequence[float]]] | None' = None,
    magmoms: 'list[Sequence[Sequence[float]]] | None' = None,
    structure_ids: 'list[str] | None' = None,
    graph_converter: 'CrystalGraphConverter | None' = None
)None

Initialize the dataset.

Args:

Raises:


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class CIFData

A dataset from CIFs.

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method __init__

__init__(
    cif_path: 'str',
    labels: 'str | dict' = 'labels.json',
    targets: 'TrainTask' = 'efsm',
    graph_converter: 'CrystalGraphConverter | None' = None,
    energy_key: 'str' = 'energy_per_atom',
    force_key: 'str' = 'force',
    stress_key: 'str' = 'stress',
    magmom_key: 'str' = 'magmom'
)None

Initialize the dataset from a directory containing CIFs.

Args:


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class GraphData

A dataset of graphs. This is compatible with the graph.pt documents made by make_graphs.py. We recommend you to use the dataset to avoid graph conversion steps.

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method __init__

__init__(
    graph_path: 'str',
    labels: 'str | dict' = 'labels.json',
    targets: 'TrainTask' = 'efsm',
    exclude: 'str | list | None' = None,
    energy_key: 'str' = 'energy_per_atom',
    force_key: 'str' = 'force',
    stress_key: 'str' = 'stress',
    magmom_key: 'str' = 'magmom'
)None

Initialize the dataset from a directory containing saved crystal graphs.

Args:


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method get_train_val_test_loader

get_train_val_test_loader(
    train_ratio: 'float' = 0.8,
    val_ratio: 'float' = 0.1,
    train_key: 'list[str] | None' = None,
    val_key: 'list[str] | None' = None,
    test_key: 'list[str] | None' = None,
    batch_size=32,
    num_workers=0,
    pin_memory=True
)tuple[DataLoader, DataLoader, DataLoader]

Partition the GraphData using materials id, randomly select the train_keys, val_keys, test_keys by train val test ratio, or use pre-defined train_keys, val_keys, and test_keys to create train, val, test loaders.

Args:

Returns: train_loader, val_loader, test_loader


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class StructureJsonData

Read structure and targets from a JSON file. This class is used to load the MPtrj dataset.

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method __init__

__init__(
    data: 'str | dict',
    graph_converter: 'CrystalGraphConverter',
    targets: 'TrainTask' = 'efsm',
    energy_key: 'str' = 'energy_per_atom',
    force_key: 'str' = 'force',
    stress_key: 'str' = 'stress',
    magmom_key: 'str' = 'magmom'
)None

Initialize the dataset by reading JSON files.

Args:


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method get_train_val_test_loader

get_train_val_test_loader(
    train_ratio: 'float' = 0.8,
    val_ratio: 'float' = 0.1,
    train_key: 'list[str] | None' = None,
    val_key: 'list[str] | None' = None,
    test_key: 'list[str] | None' = None,
    batch_size=32,
    num_workers=0,
    pin_memory=True
)tuple[DataLoader, DataLoader, DataLoader]

Partition the Dataset using materials id, randomly select the train_keys, val_keys, test_keys by train val test ratio, or use pre-defined train_keys, val_keys, and test_keys to create train, val, test loaders.

Args:

Returns: train_loader, val_loader, test_loader

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module graph.converter


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class CrystalGraphConverter

Convert a pymatgen.core.Structure to a CrystalGraph The CrystalGraph dataclass stores essential field to make sure that gradients like force and stress can be calculated through back-propagation later.

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method __init__

__init__(
    atom_graph_cutoff: 'float' = 6,
    bond_graph_cutoff: 'float' = 3,
    algorithm: "Literal['legacy', 'fast']" = 'fast',
    on_isolated_atoms: "Literal['ignore', 'warn', 'error']" = 'error',
    verbose: 'bool' = False
)None

Initialize the Crystal Graph Converter.

Args:


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method as_dict

as_dict()dict[str, str | float]

Save the args of the graph converter.


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method forward

forward(structure: 'Structure', graph_id=None, mp_id=None) → CrystalGraph

Convert a structure, return a CrystalGraph.

Args:

Return: CrystalGraph that is ready to use by CHGNet


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classmethod from_dict

from_dict(dct: 'dict') → CrystalGraphConverter

Create converter from dictionary.


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method set_isolated_atom_response

set_isolated_atom_response(
    on_isolated_atoms: "Literal['ignore', 'warn', 'error']"
)None

Set the graph converter’s response to isolated atom graph

Args:

Returns: None

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module graph.crystalgraph


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class CrystalGraph

A data class for crystal graph.

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method __init__

__init__(
    atomic_number: 'Tensor',
    atom_frac_coord: 'Tensor',
    atom_graph: 'Tensor',
    atom_graph_cutoff: 'float',
    neighbor_image: 'Tensor',
    directed2undirected: 'Tensor',
    undirected2directed: 'Tensor',
    bond_graph: 'Tensor',
    bond_graph_cutoff: 'float',
    lattice: 'Tensor',
    graph_id: 'str | None' = None,
    mp_id: 'str | None' = None,
    composition: 'str | None' = None
)None

Initialize the crystal graph.

Attention! This data class is not intended to be created manually. CrystalGraph should be returned by a CrystalGraphConverter

Args:

Raises:


property num_isolated_atoms

Number of isolated atoms given the atom graph cutoff Isolated atoms are disconnected nodes in the atom graph that will not get updated in CHGNet. These atoms will always have calculated force equal to zero.

With the default CHGNet atom graph cutoff radius, only ~ 0.1% of MPtrj dataset structures has isolated atoms.


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classmethod from_dict

from_dict(dic: 'dict[str, Any]') → CrystalGraph

Load a CrystalGraph from a dictionary.


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classmethod from_file

from_file(file_name: 'str') → CrystalGraph

Load a crystal graph from a file.

Args:

Returns:


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method save

save(fname: 'str | None' = None, save_dir: 'str' = '.')str

Save the graph to a file.

Args:

Returns:


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method to

to(device: 'str' = 'cpu') → CrystalGraph

Move the graph to a device. Default = ‘cpu’.


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method to_dict

to_dict()dict[str, Any]

Convert the graph to a dictionary.

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module graph.graph


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class Node

A node in a graph.

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method __init__

__init__(index: 'int', info: 'dict | None' = None)None

Initialize a Node.

Args:


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method add_neighbor

add_neighbor(index, edge)

Draw an directed edge between self and the node specified by index.

Args:


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class Edge

Abstract base class for edges in a graph.

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method __init__

__init__(
    nodes: 'list',
    index: 'int | None' = None,
    info: 'dict | None' = None
)None

Initialize an Edge.


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class UndirectedEdge

An undirected/bi-directed edge in a graph.

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method __init__

__init__(
    nodes: 'list',
    index: 'int | None' = None,
    info: 'dict | None' = None
)None

Initialize an Edge.


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class DirectedEdge

A directed edge in a graph.

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method __init__

__init__(
    nodes: 'list',
    index: 'int | None' = None,
    info: 'dict | None' = None
)None

Initialize an Edge.


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method make_undirected

make_undirected(index: 'int', info: 'dict | None' = None) → UndirectedEdge

Make a directed edge undirected.


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class Graph

A graph for storing the neighbor information of atoms.

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method __init__

__init__(nodes: 'list[Node]')None

Initialize a Graph from a list of nodes.


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method add_edge

add_edge(center_index, neighbor_index, image, distance)None

Add an directed edge to the graph.

Args:


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method adjacency_list

adjacency_list()

Get the adjacency list Return: graph: the adjacency list [[0, 1], [0, 2], … [5, 2] … ]] the fist column specifies center/source node, the second column specifies neighbor/destination node directed2undirected: [0, 1, …] a list of length = num_directed_edge that specifies the undirected edge index corresponding to the directed edges represented in each row in the graph adjacency list.


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method as_dict

as_dict()

Return dictionary serialization of a Graph.


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method line_graph_adjacency_list

line_graph_adjacency_list(cutoff)

Get the line graph adjacency list.

Args:

Return: line_graph: [[0, 1, 1, 2, 2], [0, 1, 1, 4, 23], [1, 4, 23, 5, 66], … … ] the fist column specifies node(atom) index at this angle, the second column specifies 1st undirected edge(left bond) index, the third column specifies 1st directed edge(left bond) index, the fourth column specifies 2nd undirected edge(right bond) index, the fifth column specifies 2nd directed edge(right bond) index,. undirected2directed: [32, 45, …] a list of length = num_undirected_edge that maps the undirected edge index to one of its directed edges indices


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method to

to(filename='graph.json')

Save graph dictionary to file.


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method undirected2directed

undirected2directed()

The index map from undirected_edge index to one of its directed_edge index.

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module model.basis


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class Fourier

Fourier Expansion for angle features.

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method __init__

__init__(order: 'int' = 5, learnable: 'bool' = False)None

Initialize the Fourier expansion.

Args:


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method forward

forward(x: 'Tensor') → Tensor

Apply Fourier expansion to a feature Tensor.


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class RadialBessel

1D Bessel Basis from: https://github.com/TUM-DAML/gemnet_pytorch/.

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method __init__

__init__(
    num_radial: 'int' = 9,
    cutoff: 'float' = 5,
    learnable: 'bool' = False,
    smooth_cutoff: 'int' = 5
)None

Initialize the SmoothRBF function.

Args:


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method forward

forward(
    dist: 'Tensor',
    return_smooth_factor: 'bool' = False
) → Tensor | tuple[Tensor, Tensor]

Apply Bessel expansion to a feature Tensor.

Args:

Returns:


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class GaussianExpansion

Expands the distance by Gaussian basis. Unit: angstrom.

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method __init__

__init__(
    min: 'float' = 0,
    max: 'float' = 5,
    step: 'float' = 0.5,
    var: 'float | None' = None
)None

Gaussian Expansion expand a scalar feature to a soft-one-hot feature vector.

Args:


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method expand

expand(features: 'Tensor') → Tensor

Apply Gaussian filter to a feature Tensor.

Args:

Returns:


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class CutoffPolynomial

Polynomial soft-cutoff function for atom graph ref: https://github.com/TUM-DAML/gemnet_pytorch/blob/-/gemnet/model/layers/envelope.py.

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method __init__

__init__(cutoff: 'float' = 5, cutoff_coeff: 'float' = 5)None

Initialize the polynomial cutoff function.

Args:


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method forward

forward(r: 'Tensor') → Tensor

Polynomial cutoff function.

Args:

Returns:

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module model.composition_model


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class CompositionModel

A simple FC model that takes in a chemical composition (no structure info) and outputs energy.

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method __init__

__init__(
    atom_fea_dim: 'int' = 64,
    activation: 'str' = 'silu',
    is_intensive: 'bool' = True,
    max_num_elements: 'int' = 94
)None

Initialize a CompositionModel.


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method forward

forward(graphs: 'list[CrystalGraph]') → Tensor

Get the energy of a list of CrystalGraphs as Tensor.


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class AtomRef

A linear regression for elemental energy. From: https://github.com/materialsvirtuallab/m3gnet/.

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method __init__

__init__(is_intensive: 'bool' = True, max_num_elements: 'int' = 94)None

Initialize an AtomRef model.


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method fit

fit(
    structures_or_graphs: 'Sequence[Structure | CrystalGraph]',
    energies: 'Sequence[float]'
)None

Fit the model to a list of crystals and energies.

Args:


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method forward

forward(graphs: 'list[CrystalGraph]')

Get the energy of a list of CrystalGraphs.

Args:

Returns: energy (tensor)


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method get_site_energies

get_site_energies(graphs: 'list[CrystalGraph]')

Predict the site energies given a list of CrystalGraphs.

Args:

Returns: a list of tensors corresponding to site energies of each graph [batchsize].


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method initialize_from

initialize_from(dataset: 'str')

Initialize pre-fitted weights from a dataset.


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method initialize_from_MPF

initialize_from_MPF()

Initialize pre-fitted weights from MPF dataset.


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method initialize_from_MPtrj

initialize_from_MPtrj()

Initialize pre-fitted weights from MPtrj dataset.


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method initialize_from_numpy

initialize_from_numpy(file_name)

Initialize pre-fitted weights from numpy file.

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module model.dynamics

Global Variables


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class CHGNetCalculator

CHGNet Calculator for ASE applications.

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method __init__

__init__(
    model: 'CHGNet | None' = None,
    use_device: 'str | None' = None,
    check_cuda_mem: 'bool' = True,
    stress_weight: 'float | None' = 0.006241509125883258,
    on_isolated_atoms: "Literal['ignore', 'warn', 'error']" = 'warn',
    **kwargs
)None

Provide a CHGNet instance to calculate various atomic properties using ASE.

Args:


property directory


property label


property n_params

The number of parameters in CHGNet.


property version

The version of CHGNet.


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method calculate

calculate(
    atoms: 'Atoms | None' = None,
    properties: 'list | None' = None,
    system_changes: 'list | None' = None
)None

Calculate various properties of the atoms using CHGNet.

Args:


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classmethod from_file

from_file(path: 'str', use_device: 'str | None' = None, **kwargs)

Load a user’s CHGNet model and initialize the Calculator.


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class StructOptimizer

Wrapper class for structural relaxation.

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method __init__

__init__(
    model: 'CHGNet | CHGNetCalculator | None' = None,
    optimizer_class: 'Optimizer | str | None' = 'FIRE',
    use_device: 'str | None' = None,
    stress_weight: 'float' = 0.006241509125883258,
    on_isolated_atoms: "Literal['ignore', 'warn', 'error']" = 'warn'
)None

Provide a trained CHGNet model and an optimizer to relax crystal structures.

Args:


property n_params

The number of parameters in CHGNet.


property version

The version of CHGNet.


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method relax

relax(
    atoms: 'Structure | Atoms',
    fmax: 'float | None' = 0.1,
    steps: 'int | None' = 500,
    relax_cell: 'bool | None' = True,
    ase_filter: 'str | None' = 'FrechetCellFilter',
    save_path: 'str | None' = None,
    loginterval: 'int | None' = 1,
    crystal_feas_save_path: 'str | None' = None,
    verbose: 'bool' = True,
    assign_magmoms: 'bool' = True,
    **kwargs
)dict[str, Structure | TrajectoryObserver]

Relax the Structure/Atoms until maximum force is smaller than fmax.

Args:

Returns: dict[str, Structure | TrajectoryObserver]: A dictionary with ‘final_structure’ and ‘trajectory’.


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class TrajectoryObserver

Trajectory observer is a hook in the relaxation process that saves the intermediate structures.

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method __init__

__init__(atoms: 'Atoms')None

Create a TrajectoryObserver from an Atoms object.

Args:


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method compute_energy

compute_energy()float

Calculate the potential energy.

Returns:


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method save

save(filename: 'str')None

Save the trajectory to file.

Args:


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class CrystalFeasObserver

CrystalFeasObserver is a hook in the relaxation and MD process that saves the intermediate crystal feature structures.

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method __init__

__init__(atoms: 'Atoms')None

Create a CrystalFeasObserver from an Atoms object.


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method save

save(filename: 'str')None

Save the crystal feature vectors to filename in pickle format.


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class MolecularDynamics

Molecular dynamics class.

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method __init__

__init__(
    atoms: 'Atoms | Structure',
    model: 'CHGNet | CHGNetCalculator | None' = None,
    ensemble: 'str' = 'nvt',
    thermostat: 'str' = 'Berendsen_inhomogeneous',
    temperature: 'int' = 300,
    starting_temperature: 'int | None' = None,
    timestep: 'float' = 2.0,
    pressure: 'float' = 0.000101325,
    taut: 'float | None' = None,
    taup: 'float | None' = None,
    bulk_modulus: 'float | None' = None,
    trajectory: 'str | Trajectory | None' = None,
    logfile: 'str | None' = None,
    loginterval: 'int' = 1,
    crystal_feas_logfile: 'str | None' = None,
    append_trajectory: 'bool' = False,
    on_isolated_atoms: "Literal['ignore', 'warn', 'error']" = 'warn',
    use_device: 'str | None' = None
)None

Initialize the MD class.

Args:

In NPT ensemble, the effective damping time for pressure is multiplied by compressibility. In LAMMPS, Bulk modulus is defaulted to 10

If bulk modulus is not provided here, it will be calculated by CHGNet through Birch Murnaghan equation of state (EOS). Note the EOS fitting can fail because of non-parabolic potential energy surface, which is common with soft system like liquid and gas. In such case, user should provide an input bulk modulus for better barostat coupling, otherwise a guessed bulk modulus = 2 GPa will be used (water’s bulk modulus)

Default = None


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method run

run(steps: 'int')None

Thin wrapper of ase MD run.

Args:


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method set_atoms

set_atoms(atoms: 'Atoms')None

Set new atoms to run MD.

Args:


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method upper_triangular_cell

upper_triangular_cell(verbose: 'bool | None' = False)None

Transform to upper-triangular cell. ASE Nose-Hoover implementation only supports upper-triangular cell while ASE’s canonical description is lower-triangular cell.

Args:


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class EquationOfState

Class to calculate equation of state.

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method __init__

__init__(
    model: 'CHGNet | CHGNetCalculator | None' = None,
    optimizer_class: 'Optimizer | str | None' = 'FIRE',
    use_device: 'str | None' = None,
    stress_weight: 'float' = 0.006241509125883258,
    on_isolated_atoms: "Literal['ignore', 'warn', 'error']" = 'error'
)None

Initialize a structure optimizer object for calculation of bulk modulus.

Args:


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method fit

fit(
    atoms: 'Structure | Atoms',
    n_points: 'int' = 11,
    fmax: 'float | None' = 0.1,
    steps: 'int | None' = 500,
    verbose: 'bool | None' = False,
    **kwargs
)None

Relax the Structure/Atoms and fit the Birch-Murnaghan equation of state.

Args:

Returns: Bulk Modulus (float)


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method get_bulk_modulus

get_bulk_modulus(unit: 'str' = 'eV/A^3')float

Get the bulk modulus of from the fitted Birch-Murnaghan equation of state.

Args:

Returns: Bulk Modulus (float)


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method get_compressibility

get_compressibility(unit: 'str' = 'A^3/eV')float

Get the bulk modulus of from the fitted Birch-Murnaghan equation of state.

Args:

Returns: Bulk Modulus (float)

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module model.encoders


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class AtomEmbedding

Encode an atom by its atomic number using a learnable embedding layer.

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method __init__

__init__(atom_feature_dim: 'int', max_num_elements: 'int' = 94)None

Initialize the Atom featurizer.

Args:


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method forward

forward(atomic_numbers: 'Tensor') → Tensor

Convert the structure to a atom embedding tensor.

Args:

Returns:


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class BondEncoder

Encode a chemical bond given the positions of two atoms using Gaussian distance.

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method __init__

__init__(
    atom_graph_cutoff: 'float' = 5,
    bond_graph_cutoff: 'float' = 3,
    num_radial: 'int' = 9,
    cutoff_coeff: 'int' = 5,
    learnable: 'bool' = False
)None

Initialize the bond encoder.

Args:


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method forward

forward(
    center: 'Tensor',
    neighbor: 'Tensor',
    undirected2directed: 'Tensor',
    image: 'Tensor',
    lattice: 'Tensor'
)tuple[Tensor, Tensor, Tensor]

Compute the pairwise distance between 2 3d coordinates.

Args:

Returns:


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class AngleEncoder

Encode an angle given the two bond vectors using Fourier Expansion.

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method __init__

__init__(num_angular: 'int' = 9, learnable: 'bool' = True)None

Initialize the angle encoder.

Args:


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method forward

forward(bond_i: 'Tensor', bond_j: 'Tensor') → Tensor

Compute the angles between normalized vectors.

Args:

Returns:

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module model.functions


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function aggregate

aggregate(
    data: 'Tensor',
    owners: 'Tensor',
    average=True,
    num_owner=None
) → Tensor

Aggregate rows in data by specifying the owners.

Args:

Returns:


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function find_activation

find_activation(name: 'str') → Module

Return an activation function using name.


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function find_normalization

find_normalization(name: 'str', dim: 'int | None' = None) → Module | None

Return an normalization function using name.


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class MLP

Multi-Layer Perceptron used for non-linear regression.

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method __init__

__init__(
    input_dim: 'int',
    output_dim: 'int' = 1,
    hidden_dim: 'int | Sequence[int] | None' = (64, 64),
    dropout: 'float' = 0,
    activation: 'str' = 'silu',
    bias: 'bool' = True
)None

Initialize the MLP.

Args:


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method forward

forward(X: 'Tensor') → Tensor

Performs a forward pass through the MLP.

Args:

Returns:


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class GatedMLP

Gated MLP similar model structure is used in CGCNN and M3GNet.

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method __init__

__init__(
    input_dim: 'int',
    output_dim: 'int',
    hidden_dim: 'int | list[int] | None' = None,
    dropout: 'float' = 0,
    activation: 'str' = 'silu',
    norm: 'str' = 'batch',
    bias: 'bool' = True
)None

Initialize a gated MLP.

Args:


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method forward

forward(X: 'Tensor') → Tensor

Performs a forward pass through the MLP.

Args:

Returns:


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class ScaledSiLU

Scaled Sigmoid Linear Unit.

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method __init__

__init__()None

Initialize a scaled SiLU.


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method forward

forward(x: 'Tensor') → Tensor

Forward pass.

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module model.layers


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class AtomConv

A convolution Layer to update atom features.

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method __init__

__init__(
    atom_fea_dim: 'int',
    bond_fea_dim: 'int',
    hidden_dim: 'int' = 64,
    dropout: 'float' = 0,
    activation: 'str' = 'silu',
    norm: 'str | None' = None,
    use_mlp_out: 'bool' = True,
    mlp_out_bias: 'bool' = False,
    resnet: 'bool' = True,
    gMLP_norm: 'str | None' = None
)None

Initialize the AtomConv layer.

Args:


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method forward

forward(
    atom_feas: 'Tensor',
    bond_feas: 'Tensor',
    bond_weights: 'Tensor',
    atom_graph: 'Tensor',
    directed2undirected: 'Tensor'
) → Tensor

Forward pass of AtomConv module that updates the atom features and optionally bond features.

Args:

Returns:

Notes:

  • num_batch_atoms = sum(num_atoms) in batch

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class BondConv

A convolution Layer to update bond features.

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method __init__

__init__(
    atom_fea_dim: 'int',
    bond_fea_dim: 'int',
    angle_fea_dim: 'int',
    hidden_dim: 'int' = 64,
    dropout: 'float' = 0,
    activation: 'str' = 'silu',
    norm: 'str | None' = None,
    use_mlp_out: 'bool' = True,
    mlp_out_bias: 'bool' = False,
    resnet=True,
    gMLP_norm: 'str | None' = None
)None

Initialize the BondConv layer.

Args:


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method forward

forward(
    atom_feas: 'Tensor',
    bond_feas: 'Tensor',
    bond_weights: 'Tensor',
    angle_feas: 'Tensor',
    bond_graph: 'Tensor'
) → Tensor

Update the bond features.

Args:

Returns:

Notes:

  • num_batch_atoms = sum(num_atoms) in batch

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class AngleUpdate

Update angle features.

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method __init__

__init__(
    atom_fea_dim: 'int',
    bond_fea_dim: 'int',
    angle_fea_dim: 'int',
    hidden_dim: 'int' = 0,
    dropout: 'float' = 0,
    activation: 'str' = 'silu',
    norm: 'str | None' = None,
    resnet: 'bool' = True,
    gMLP_norm: 'str | None' = None
)None

Initialize the AngleUpdate layer.

Args:


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method forward

forward(
    atom_feas: 'Tensor',
    bond_feas: 'Tensor',
    angle_feas: 'Tensor',
    bond_graph: 'Tensor'
) → Tensor

Update the angle features using bond graph.

Args:

Returns:

Notes:

  • num_batch_atoms = sum(num_atoms) in batch

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class GraphPooling

Pooling the sub-graphs in the batched graph.

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method __init__

__init__(average: 'bool' = False)None

Args: average (bool): whether to average the features.


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method forward

forward(atom_feas: 'Tensor', atom_owner: 'Tensor') → Tensor

Merge the atom features that belong to same graph in a batched graph.

Args:

Returns:


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class GraphAttentionReadOut

Multi Head Attention Read Out Layer merge the information from atom_feas to crystal_fea.

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method __init__

__init__(
    atom_fea_dim: 'int',
    num_head: 'int' = 3,
    hidden_dim: 'int' = 32,
    average=False
)None

Initialize the layer.

Args:


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method forward

forward(atom_feas: 'Tensor', atom_owner: 'Tensor') → Tensor

Merge the atom features that belong to same graph in a batched graph.

Args:

Returns:

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module model.model

Global Variables


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class CHGNet

Crystal Hamiltonian Graph neural Network A model that takes in a crystal graph and output energy, force, magmom, stress.

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method __init__

__init__(
    atom_fea_dim: 'int' = 64,
    bond_fea_dim: 'int' = 64,
    angle_fea_dim: 'int' = 64,
    composition_model: 'str | Module' = 'MPtrj',
    num_radial: 'int' = 31,
    num_angular: 'int' = 31,
    n_conv: 'int' = 4,
    atom_conv_hidden_dim: 'Sequence[int] | int' = 64,
    update_bond: 'bool' = True,
    bond_conv_hidden_dim: 'Sequence[int] | int' = 64,
    update_angle: 'bool' = True,
    angle_layer_hidden_dim: 'Sequence[int] | int' = 0,
    conv_dropout: 'float' = 0,
    read_out: 'str' = 'ave',
    mlp_hidden_dims: 'Sequence[int] | int' = (64, 64, 64),
    mlp_dropout: 'float' = 0,
    mlp_first: 'bool' = True,
    is_intensive: 'bool' = True,
    non_linearity: "Literal['silu', 'relu', 'tanh', 'gelu']" = 'silu',
    atom_graph_cutoff: 'float' = 6,
    bond_graph_cutoff: 'float' = 3,
    graph_converter_algorithm: "Literal['legacy', 'fast']" = 'fast',
    cutoff_coeff: 'int' = 8,
    learnable_rbf: 'bool' = True,
    gMLP_norm: 'str | None' = 'layer',
    readout_norm: 'str | None' = 'layer',
    version: 'str | None' = None,
    **kwargs
)None

Initialize CHGNet.

Args:


property n_params

Return the number of parameters in the model.


property version

Return the version of the loaded checkpoint.


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method as_dict

as_dict()dict

Return the CHGNet weights and args in a dictionary.


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method forward

forward(
    graphs: 'Sequence[CrystalGraph]',
    task: 'PredTask' = 'e',
    return_site_energies: 'bool' = False,
    return_atom_feas: 'bool' = False,
    return_crystal_feas: 'bool' = False
)dict[str, Tensor]

Get prediction associated with input graphs

Args:

Returns: model output (dict).


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classmethod from_dict

from_dict(dct: 'dict', **kwargs) → CHGNet

Build a CHGNet from a saved dictionary.


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classmethod from_file

from_file(path: 'str', **kwargs) → CHGNet

Build a CHGNet from a saved file.


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classmethod load

load(
    model_name='0.3.0',
    use_device: 'str | None' = None,
    check_cuda_mem: 'bool' = True,
    verbose: 'bool' = True
) → CHGNet

Load pretrained CHGNet model.

Args: model_name (str, optional): Default = “0.3.0”.

Raises:


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method predict_graph

predict_graph(
    graph: 'CrystalGraph | Sequence[CrystalGraph]',
    task: 'PredTask' = 'efsm',
    return_site_energies: 'bool' = False,
    return_atom_feas: 'bool' = False,
    return_crystal_feas: 'bool' = False,
    batch_size: 'int' = 16
)dict[str, Tensor] | list[dict[str, Tensor]]

Predict from CrustalGraph.

Args:

Returns:


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method predict_structure

predict_structure(
    structure: 'Structure | Sequence[Structure]',
    task: 'PredTask' = 'efsm',
    return_site_energies: 'bool' = False,
    return_atom_feas: 'bool' = False,
    return_crystal_feas: 'bool' = False,
    batch_size: 'int' = 16
)dict[str, Tensor] | list[dict[str, Tensor]]

Predict from pymatgen.core.Structure.

Args:

Returns:


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method todict

todict()dict

Needed for ASE JSON serialization when saving CHGNet potential to trajectory file (https://github.com/CederGroupHub/chgnet/issues/48).


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class BatchedGraph

Batched crystal graph for parallel computing.

Attributes:

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method __init__

__init__(
    atomic_numbers: 'Tensor',
    bond_bases_ag: 'Tensor',
    bond_bases_bg: 'Tensor',
    angle_bases: 'Tensor',
    batched_atom_graph: 'Tensor',
    batched_bond_graph: 'Tensor',
    atom_owners: 'Tensor',
    directed2undirected: 'Tensor',
    atom_positions: 'Sequence[Tensor]',
    strains: 'Sequence[Tensor]',
    volumes: 'Sequence[Tensor] | Tensor'
)None

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classmethod from_graphs

from_graphs(
    graphs: 'Sequence[CrystalGraph]',
    bond_basis_expansion: 'Module',
    angle_basis_expansion: 'Module',
    compute_stress: 'bool' = False
) → BatchedGraph

Featurize and assemble a list of graphs.

Args:

Returns:

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module trainer.trainer


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class Trainer

A trainer to train CHGNet using energy, force, stress and magmom.

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method __init__

__init__(
    model: 'CHGNet | None' = None,
    targets: 'TrainTask' = 'ef',
    energy_loss_ratio: 'float' = 1,
    force_loss_ratio: 'float' = 1,
    stress_loss_ratio: 'float' = 0.1,
    mag_loss_ratio: 'float' = 0.1,
    optimizer: 'str' = 'Adam',
    scheduler: 'str' = 'CosLR',
    criterion: 'str' = 'MSE',
    epochs: 'int' = 50,
    starting_epoch: 'int' = 0,
    learning_rate: 'float' = 0.001,
    print_freq: 'int' = 100,
    torch_seed: 'int | None' = None,
    data_seed: 'int | None' = None,
    use_device: 'str | None' = None,
    check_cuda_mem: 'bool' = True,
    **kwargs
)None

Initialize all hyper-parameters for trainer.

Args:


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method get_best_model

get_best_model() → CHGNet

Get best model recorded in the trainer.


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classmethod load

load(path: 'str') → Trainer

Load trainer state_dict.


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method move_to

move_to(obj, device) → Tensor | list[Tensor]

Move object to device.


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method save

save(filename: 'str' = 'training_result.pth.tar')None

Save the model, graph_converter, etc.


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method save_checkpoint

save_checkpoint(
    epoch: 'int',
    mae_error: 'dict',
    save_dir: 'str | None' = None
)None

Function to save CHGNet trained weights after each epoch.

Args:


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method train

train(
    train_loader: 'DataLoader',
    val_loader: 'DataLoader',
    test_loader: 'DataLoader | None' = None,
    save_dir: 'str | None' = None,
    save_test_result: 'bool' = False,
    train_composition_model: 'bool' = False
)None

Train the model using torch data_loaders.

Args:


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class CombinedLoss

A combined loss function of energy, force, stress and magmom.

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method __init__

__init__(
    target_str: 'str' = 'ef',
    criterion: 'str' = 'MSE',
    is_intensive: 'bool' = True,
    energy_loss_ratio: 'float' = 1,
    force_loss_ratio: 'float' = 1,
    stress_loss_ratio: 'float' = 0.1,
    mag_loss_ratio: 'float' = 0.1,
    delta: 'float' = 0.1
)None

Initialize the combined loss.

Args:


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method forward

forward(
    targets: 'dict[str, Tensor]',
    prediction: 'dict[str, Tensor]'
)dict[str, Tensor]

Compute the combined loss using CHGNet prediction and labels this function can automatically mask out magmom loss contribution of data points without magmom labels.

Args:

Returns: dictionary of all the loss, MAE and MAE_size

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module utils.common_utils


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function determine_device

determine_device(use_device: 'str | None' = None, check_cuda_mem: 'bool' = True)

Determine the device to use for torch model.

Args:

Returns:


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function cuda_devices_sorted_by_free_mem

cuda_devices_sorted_by_free_mem()list[int]

List available CUDA devices sorted by increasing available memory.

To get the device with the most free memory, use the last list item.


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function mae

mae(prediction: 'Tensor', target: 'Tensor') → Tensor

Computes the mean absolute error between prediction and target.

Args:

Returns: tensor


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function read_json

read_json(filepath: 'str')dict

Read the json file.

Args:

Returns:


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function write_json

write_json(dct: 'dict', filepath: 'str')dict

Write the json file.

Args:

Returns: written dictionary


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function mkdir

mkdir(path: 'str')str

Make directory.

Args:

Returns: path


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class AverageMeter

Computes and stores the average and current value.

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method __init__

__init__()None

Initialize the meter.


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method reset

reset()None

Reset the meter value, average, sum and count to 0.


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method update

update(val: 'float', n: 'int' = 1)None

Update the meter value, average, sum and count.

Args:

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module utils.vasp_utils


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function parse_vasp_dir

parse_vasp_dir(
    file_root: 'str',
    check_electronic_convergence: 'bool' = True
)dict[str, list]

Parse VASP output files into structures and labels By default, the magnetization is read from mag_x from VASP, plz modify the code if magnetization is for (y) and (z).

Args:


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function solve_charge_by_mag

solve_charge_by_mag(
    structure: 'Structure',
    default_ox: 'dict[str, float] | None' = None,
    ox_ranges: 'dict[str, dict[tuple[float, float], int]] | None' = None
) → Structure | None

Solve oxidation states by magmom.

Args: