Dataset#

This Module contains classes and functions to deal with dataset.

A dataset is comprised of a set of configurations, which provide the training data to optimize potential (parameters) or provide the test data to test the quality of potential.

A configuration should have three lattice vectors of the simulation cell, flags to indicate periodic boundary conditions (PBC), species and coordinates of all atoms. These collectively define a configuration and are, typically, considered as the input in terms of potential fitting. A configuration should also contain a set of output (target), which the optimization algorithm adjust the potential (parameters) to match. For example, if the force-matching scheme is used for the fitting, the output can be the forces on individual atoms. The currently supported outputs include energy, forces, and stress.

See also

See kliff.dataset.Configuration for a complete list of the member functions of the Configuration class.

To create a data, do:

from kliff.dataset import Dataset
path = 'path_to_my_dataset_files'
dset = Dataset(path, format='extxyz')

where path is a file storing a configuration or a directory containing multiple files. If given a directory, all the files in this directory and its subdirectories with the extension corresponding to the specified format will be read. For example, if format='extxyz', all the files with an extension .xyz in path and its subdirectories will be read.

The size of the dataset can be obtained by:

dset_size = dset.get_num_configs()

and a list of configurations constituting the dataset can be obtained by:

configs = dset.get_configs()

See also

See kliff.dataset.Dataset for a complete list of the member functions of the Dataset class.

Inspect dataset#

KLIFF provides a command line tool to get a statistics of a dataset of files. For example, for the Si_training_set.tar.gz (the tarball can be extracted by: $ tar xzf Si_training_set.tar.gz), running:

$ kliff dataset --count Si_training_set

prints out the below information:

================================================================================
                             KLIFF Dataset Count

Notation: "──dir_name (a/b)"
a: number of .xyz files in the directory "dir_name"
b: number of .xyz files in the directory "dir_name" and its subdirectories

Si_training_set (0/1000)
├──NVT_runs (600/600)
└──varying_alat (400/400)

================================================================================

Dataset Format#

More than often, your dataset is generated from first-principles calculations using packages like VASP, SIESTA, and Quantum Espresso among others. Their output file format may not be support by KLIFF. You can use parse these output to get the necessary data, and then convert to the format supported by KLIFF using the functions kliff.dataset.write_config() and kliff.dataset.read_config().

Currently supported dataset format include:

  • extended XYZ (.xyz)

Extended XYZ#

The Extended XYZ format is an enhanced version of the basic XYZ format that allows extra columns to be present in the file for additional per-atom properties as well as standardizing the format of the comment line to include the cell lattice and other per-frame parameters. It typically has the .xyz extension.

It would be easy to explain the format with an example. Below is an example of the extended XYZ format supported by KLIFF:

8
Lattice="4.8879 0 0 0 4.8879 0 0 0 4.8879"  PBC="1 1 1"  Energy=-29.3692121943  Properties=species:S:1:pos:R:3:force:R:3
Si    0.00000e+00   0.00000e+00   0.00000e+00  2.66454e-15  -8.32667e-17   4.02456e-16
Si    2.44395e+00   2.44395e+00   0.00000e+00  1.62370e-15   7.21645e-16   8.46653e-16
Si    0.00000e+00   2.44395e+00   2.44395e+00  0.00000e+00   3.60822e-16   2.01228e-16
Si    2.44395e+00   0.00000e+00   2.44395e+00  1.33227e-15  -4.44089e-16   8.74350e-16
Si    1.22198e+00   1.22198e+00   1.22198e+00  4.44089e-15   1.80411e-16   1.87350e-16
Si    3.66593e+00   3.66593e+00   1.22198e+00  9.29812e-16  -2.67841e-15  -3.22659e-16
Si    1.22198e+00   3.66593e+00   3.66593e+00  5.55112e-17   3.96905e-15   8.87786e-16
Si    3.66593e+00   1.22198e+00   3.66593e+00 -2.60902e-15  -9.43690e-16   6.37999e-16
  • The first line list the number of atoms in the system.

  • The second line follow the key=value structure. if a value contains any space (e.g. Lattice), it should be placed in the quotation marks " ". The supported keys are:

    • Lattice represents the three Cartesian lattice vectors: the first 3 numbers denote \bm a_1, the next three numbers denote \bm a_2, and the last 3 numbers denote \bm a_3. Note that \bm a_1, \bm a_2, and \bm a_3 should follow the right-hand rule such that the volume of the cell can be obtained by (\bm a_1\times \bm a_2)\cdot \bm a_3.

    • PBC. Three integers of 1 or 0 (or three characters of T or F) to indicate whether to use periodic boundary conditions along \bm a_1, \bm a_2, and $\bm a_3$, respectively.

    • Energy. A real value of the total potential energy of the system.

    • Properties provides information of the names, size, and types of the data that are listed in the body part of the file. For example, the Properties in the above example means that the atomic species information (a string) is listed in the first column of the body, the next three columns list the atomic coordinates, and the last three columns list the forces on atoms.

Each line in the body lists the information, indicated by Properties in the second line, for one atom in the system, taking the form:

species  x  y  z  fx  fy  fz

The coordinates x  y  z should be given in Cartesian values, not fractional values. The forces fx fy fz can be skipped if you do not want to use them.

Note

An atomic configuration stored in the extended XYZ format can be visualized using the OVITO program.

Weight#

As mentioned in Theory, the reference \bm q can be any material properties, which can carry different physical units. The weight in the loss function can be used to put quantities with different units on a common scale. The weights also give us access to set which properties or configurations are more important, for example, in developing a potential for a certain application (see Define your weight class).

KLIFF uses weight class to compute and store the weight information for each configuration. The basic structure of the class is shown below.

class Weight():
    """A class to deal with weights for each configuration."""

    def __init__(self):
        #... Do necessary steps to initialize the class

    def compute_weight(self, config):
        #... Compute the weights for the given configutation

    @property
    def some_weight(self):
        #... Add properties to retrieve the weight values

Default weight class#

KLIFF has several built-in weight classes. As a default, KLIFF uses kliff.dataset.weight.Weight, which put a single weight for each property.

from kliff.dataset import Dataset
from kliff.dataset.weight import  Weight

path = 'path_to_my_dataset_files'
weight = Weight()
dset = Dataset(path, weight=weight, format='extxyz')

# Retrieve the weights
config_weight = configs[0].config_weight
energy_weight = configs[0].energy_weight
forces_weight = configs[0].forces_weight
stress_weight = configs[0].stress_weight

config_weight is the weight for the configuration and energy_weight, forces_weight, and stress_weigth are the weights for energy, forces, and stress, respectively. The default value for each weight is 1.0.

One can also specify different values for these weights. For example, one might want to weigh the energy 10 times as the forces. It can be done by specifying the weight values while instantiating kliff.dataset.weight.Weight.

weight = Weight(
    config_weight=1.0, energy_weight=10.0, forces_weight=1.0, stress_weight=1.0
)

Note

Another use case is if one wants to, for example, exclude the energy in the loss function, which can be done by setting energy_weight=0.0.

Magnitude-inverse weight#

KLIFF also provides another weight class that computes the weight based on the magnitude of the data, applying different weight on each data point. The weight calculation is motivated by formulation suggested by Lenosky et al. [lenosky1997],

\frac{1}{w_i}^2 = c_1^2 + c_2^2 \| \bm p_i \|^2

c_1 and c_2 are parameters to compute the weight. They can be thought as a padding and a fractional scaling terms. When \bm p_i corresponds to energy, the norm is the absolute value of the energy. When \bm p_i correspond to forces, the norm is a vector norm of the force vector acting on the corresponding atom. This also mean that each force component acting on the same atom will have the same weight. If \bm p_i correspond to stress, then the norm is a Frobenius norm of the stress tensor, giving the same weight for each component in the stress tensor.

To use this weight, we instantiate MagnitudeInverseWeight weight class:

from kliff.dataset.weight import MagnitudeInverseWeight
weight = MagnitudeInverseWeight(
    config_weight=1.0,
    weight_params={
        "energy_weight_params": [c1e, c2e],
        "forces_weight_params": [c1f, c2f],
        "stress_weight_params": [c1s, c2s],
    }
)

config_weight specifies the weight for the entire configuration.

weight_params is a dictionary containing c_1 and c_2 for energy, forces, and stress. The default value is:

weight_params = {
    "energy_weight_params": [1.0, 0.0],
    "forces_weight_params": [1.0, 0.0],
    "stress_weight_params": [1.0, 0.0],
}

Additionally, for each key, we can pass in a float, which set the value of c_1 with c_2=0.0.

lenosky1997

Lenosky, T.J., Kress, J.D., Kwon, I., Voter, A.F., Edwards, B., Richards, D.F., Yang, S., Adams, J.B., 1997. Highly optimized tight-binding model of silicon. Phys. Rev. B 55, 15281544. https://doi.org/10.1103/PhysRevB.55.1528

Define your weight class#

We can also define a custom weight class to use in KLIFF. As an example, suppose we are developing a potential that will be used to investigate fracture properties. The training sets includes both configurations with and without cracks. For this application, we might want to put larger weights for the configurations with cracks. Below is an example of weight class that achieve this goal.

from kliff.dataset.weight import Weight

class WeightForCracks(Weight):
    """An example weight class that put larger weight on the configurations with
    cracks. This class inherit from ``kliff.dataset.weight.Weight``. We just need to
    modify ``compute_weight`` method to put larger weight for the configurations with
    cracks. Other modifications might need to be done for different weight class.
    """

    def __init__(self, energy_weight, forces_weight):
        super().__init__(energy_weight=energy_weight, forces_weight=forces_weight)

    def compute_weight(self, config):
        identifier = config.identifer
        if 'with_cracks' in identifier:
            self._config_weight = 10.0

With this weight class, we can use the built-in residual_fn to achieve the same result as the implementation in Use your own residual function.