kpLib 1.1.0

Library for generating highly-efficient generalized Monkhorst-Pack K-point grids to accelerate electronic structure calculations, like DFT.

KpLib

KpLib is a C++ library for finding the optimal Generalized Monkhorst-Pack k-points grid. It can be imported into electronic-structure packages as a generator of efficient generalized k-point grids, or be integrated into user scripts through the Python interface kpLib.

For questions of KpLib and underlying algorithms, you are welcomed to check our paper at here or send emails to [e-mail].

Note: We have fixed in v.1.1.0 (2023.04.08) a few bugs of the Python interface causing scripts to hang for monoclinic and trigonal system in $hR$ representation. We also reduced dependencies of external Python packages to the minimum to let users choose their favoriate package for structure IO. Please check the RELEASE.md note for details.

Usage

Route I: Integrating KpLib as a C++ library in simulation package

---

Compile KpLib as a library

We use cmake to detect native build environment and generate native build files. For Unix-like operating systems, users can build the project by:

$ git clone https://gitlab.com/muellergroup/kplib.git
$ cd kplib
$ mkdir build
$ cd build
$ cmake ..
$ make

Then you can find a static library at build/libkpoints.a and a dynamic library at build/libkpoints.so.

Use KpLib in C++ Code

There are basically two steps:

1. Copy the header file src/kPointLatticeGenerator.h to your include folder, and add the following line to your source code

    #include "kPointLatticeGenerator.h"
   

2. Link the library at the linking stage.

   For example, to link the static library and compile the object myapp.o to the final executable myapp, you can

    $ g++ myapp.o -L /path/to/lib libkpoints.a -o myapp
   

   If you want to use the dynamic library, you can

    $ export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/path/to/lib
    $ g++ myapp.o -L /path/to/lib -lkpoints -o myapp
   

   The first line tells the loader, ld, where to find the shared library at runtime, since the dynamic linkage only puts a reference of the library in the executable.

Then you are ready to go!

An explanation of the API with more details can be found below, together with a demo C++ program (demo_kplib/src) created to demonstrate the usage of this API in C++ environment and the conventions the API assumes.

Route II: Use KpLib as a Python package

---

Installation

Install from PyPI using pip

This will install the core KpLib module with minimal third-party dependencies. User will need to parse input structures and write out generated K-point grid using their favorite matsci packages (e.g. ase and pymatgen).

$ pip install --user pybind11 build wheel
$ pip install --user kplib

Install from source using pip

The Python interface is a thin wrapper of the C++ library. Building from source should be straight forward. It also provides a way to install a CLI command to be called in terminal (requiring pymatgen for IO).

Step 1. Download source code from https://gitlab.com/muellergroup/kplib.

$ git clone https://gitlab.com/muellergroup/kplib.git

Step 2. The C++-Python interface is written using pybind11, and the setup.py imports it at the beginning for compiling the C++ code into a Python external module. So, it should be installed before installing kpLib.

$ pip install --user pybind11
$ pip install --user build wheel # Python build toolchain that you might already have.

Step 3. If you would like to choose your own structure parser (such as pymatgen, ase and other wonderful packages), you can install only the core module of kpLib with a minimal dependencies:

$ cd kplib
$ pip install --user .

If you would like to use the CLI command tool kpgen, you can use the following code to install kpLib. This will additionally install pymatgen (as a parser) and click (to provide a nice CLI interface):

$ cd kplib
$ pip install --user .[cli]
$ which kpgen

If you want to test your installation, you can install kpLib with dependencies that enables the test suite (pymatgen and pytest). After installation, you can run tests to make sure everything is correctly installed.

$ cd kplib
$ pip install --user .[tests]
$ pytest

Using KpLib in Python script

The kpLib Python package is written in the way to provide an interface for the core KpLib function and allow maximal usage flexibility. The choice of packages for parsing input structure and for writing the generated k-points grid to a desired format is left to users. The following section will first describe the signature of the get_kpoints function, and then give two example scripts using the two popular packages pymatgen and ase for I/O.

kpLib.interface::get_kpoints is the core and only function the kpLib Python package provides. Its signature is as follows:

def get_kpoints(
    lattice:            List[List[float]] or numpy.ndarray,
    fractional_coords:  List[List[float]] or numpy.ndarray,
    atomic_numbers:     List[int] or Tuple[int],
    min_distance:       float = 0.1,
    min_total_kpoints:  int = 1,
    include_gamma:      str = "auto",
    symprec:            float = 1e-5,
    use_scale_factor:   bool = False,
) -> Dict

get_kpoints uses the 3 positional arguments to define a structure. The rest of the keyword arguments define requirements of the output K-point grid. The size requirement of K-point grids is defined by min_distance and min_total_kpoints. They can be used separately or together. We recommand using min_distance. For an example of correlating the conventional way of specifying a Monkhorst-Pack grid by three integers m1 x m2 x m3 to min_distance, see the section below.

The meaning of all arguments are as follows:

• lattice: A 3x3 matrix representing the lattice vectors of the unit cell of the input structure. Each row represents a vector, i.e. in the format of [a, b, c]. It can be a list of list or a numpy.ndarray.
• fractional_coords: A list of 3-dimensional coordinates in the fractional format relative to the lattice vectors. It can be a list of list or a numpy.ndarray.
• atomic_numbers: A list of atomic numbers, the i-th number of which defines the element of the i-th atom at the position fractional_coords[i]. It can be a list or a tuple.
• min_distance: The minimum required distance in real-space between any pair of lattice points in the superlattice corresponding to the returned K-point grid. It specifys the size of the required K-point grid and has a unit of Angstrom. The larger the value of min_distance, the denser the returned grid will be (i.e., more symmetrically distinct K-point).
• min_total_kpoints: Minimum number of total K-points required in the returned K-point grid. Note, it is not the number of symmetrically distinct K-points that are actually used in a calculation. The total set of K-points will be reduced by symmetry to the minimal set of distinct K-points, which will be used in a calculation.
• include_gamma: It can have values true or false, or auto. Use true to include the Gamma point (i.e. [0, 0, 0] of reciprocal space) in the return grid. Use false to exclude the Gamma point. Use auto to let kplib choose between the two types of grid and return the one with a smaller number of symmetrically distinct K-points. If they have the same number of distinct K-points, return the one with larger effecitive min_distance.
• symprec: Precision in real space when determining structure symmetries. The default is 1E-5. Atomic coordinates with a difference less than symprec will be determined as equivalent. If you want to include more symmetries, use a larger value.
• use_scale_factor: A scheme to speed up the search at the cost of returning a sub-optimial K-point grid. Default behaviour is to not use the scheme. You can consider use this scheme when the code takes a very long time to return a grid. For a detailed explanation, see the end of this README.

The get_kpoints function returns the K-point grid as a dict. The keys are:

• min_periodic_distance: The minimum distance between any pair of lattice points of the real-space superlattice corresponding to the returned K-point grid.
• num_distinct_kpts: The number of symmetrically distinct K-points. This is the number of K-points actually used in a calculation.
• distinct_coords: Fractional coordinates of symmetrically distinct K-points.
• distinct_weights: Weights of corresponding symmetrically distinct K-point in distinct_coords.
• num_total_kpts: The number of total K-points.
• coords: Fractional coordinates of all K-points.
• weights: Weights of corresponding K-points in coords.

Usually the coordinates and weights of distinct K-points are enough to define an input file of a calculation.

Making sense of "min_distance"

"Minimum distance" means the minimum periodic distance between any pairs of lattice points on a real-space lattice. The reciprocal lattice of a primitive unit cell is a supercell in the reciprocal space of the reciprocal lattice of a super cell in real space. It implys each K-point grid corresponds to a real-space superlattice. Therefore, we can use the "minimum distance" of a real-space superlattice to specify the size its corresponding K-point grid in the reciprocal space. The larger the min_distance, the larger the supercell and hence the denser the K-point grid.

For example, consider an artificial tetragonal structure with a lattice as $[[2, 0, 0], [0, 2, 0], [0, 0, 3]]$ and a single atom at the origin. Its space group is $P4/mmm$. A traditional Monkhorst-Pack 4x4x4 grid with the Gamma point would correspond to a supercell of $[[8, 0, 0], [0, 8, 0], [0, 0, 12]]$ and has 18 symmetrically distinct K-points. The minimum periodic distance in this case is 8 Angstroms.

When using kpLib with min_distance = 8, the Gamma-centered result would have only 9 symmetrically distinct K-points and an effective minimum distance of 8.485. For shifted grids, the result would be a grid with 6 symmetrically distinct K-points and an effective minimum distance of 8. Similar calculation accuracy but three times less the number of distinct K-points.

Example Python Scripts

Here, we give two examples of using kpLib in Python script, using the popular pymatgen and ase packages respectively for IO.

from kplib import get_kpoints
from pymatgen.core import Structure
from pymatgen.io.vasp.inputs import Kpoints, Kpoints_supported_modes

struct = Structure.from_file("./POSCAR")

kpts = get_kpoints(struct.lattice.matrix,
                   struct.frac_coords,
                   struct.atomic_numbers, 
                   min_distance=25.0, 
                   include_gamma="auto")

kpoints_file = Kpoints(style=Kpoints_supported_modes.Reciprocal,
                       num_kpts=kpts["num_distinct_kpts"]
                       kpts=kpts["distinct_coords"],
                       kpts_weights=["distinct_weights"])
kpoints_file.write_file("./KPOINTS")

from kpLib.interface import get_kpoints
import ase

struct = ase.io.read("./POSCAR", format="vasp")
kpts = get_kpoints(struct.get_cell()[:], 
                   struct.get_scaled_positions(), # Note: the atomic coordinates are in fractional format, not Cartesian.
                   struct.get_atomic_numbers(), 
                   min_distance=25.0,
                   include_gamma="gamma")

# ... User can then use the kpts dict to build VASP calculator and perform other tasks

Command-line interface kpgen

For users' convenience, the Python package also provides a command-line interface that can be called in terminal. It uses pymatgen as a structure parser and output the K-point grid to a vasp-format KPOINTS file.

A simple example is:

$ kpgen -g auto -d 25.0 ./POSCAR ./KPOINTS

-g is short for --gamma and it defined the type of K-point grid, a gamma-centered, shifted or auto-determined grid. -d is short for --distance and specifies min_distance. This is a simple wrapper of the get_kpionts function discussed above. All the options that get_kpoints accepts, this CLI accepts. Use kpgen --help to get a full list of permitted arguments and options.

The default pip install kplib does not install this CLI. To enable this feature, after cloning the source code, run:

$ pip install --user .[cli]

The "[cli]" part instructs the setup.py to install pymatgen and click.

How to cite

If you use the kplib or the K-point Grid Generator in generating generalized k-point grids, please cite the following paper:

Wang, Y., Wisesa, P., Balasubramanian, A., Dwaraknath, S. & Mueller, T. Rapid generation of optimal generalized Monkhorst-Pack grids. Comp Mater Sci, 110100, doi:https://doi.org/10.1016/j.commatsci.2020.110100 (2020).

Documentation

Conventions

This section specifies the conventions we assume in the arguments to the KPointLatticeGenerator constructor in C++. The Python interface kpLib is written in the same manner that aligns with these conventions.

Lattice Vectors

Lattice vectors are expressed as row vectors in the lattice matrix:

double primtiveVectors[3][3] = {{a_x, a_y, a_z},
                                {b_x, b_y, b_z},
                                {c_a, c_z, c_y}}

Point Operators

Each operatir is a 3x3 integer matrix, representing how a fractional coordinate in the primitive lattice basis is transformed under this operation.

int latticePointOperations[][3][3];

Because of the lattice vectors are expressed as rows, each symmetry operation is done through ${x'}^T = {x}^T\cdot R$.

Conventional Lattice Vectors

The algorithm we developed to efficiently iterate symmetry-preserving superlattices assumes the following conditions on the input conventional lattice vectors, additional to the usual conventions (e.g., see the standard unit cell in spglib documentation).

• For all crystal sysmtes, except for triclinic, the $\mathbf{c}$-vector should be along the axis of the highest-order rotational operation, i.e the 4-fold, 6-fold, 3-fold, 4-fold, any 2-fold, and 2-fold rotation for cubic, hexagonal, trigonal, tetragonal, orthorhombic, and monoclinic lattices, respectively. This direction is commonly referred as the "primary symmetry direction" in crystallography textbooks. The conventions in the International Tables of Crystallograph (see Chapter 9.1) already satisfy this except for monoclinic lattice.

• For monoclinic crystal system, the international convention assumes the $\mathbf{b}$-unique setting, in which the $\beta$ angle is the non-acute angle, the two-fold rotation axis is along the $\mathbf{b}$ vector and the one-face-centered lattice point in the $\mathbf{a}$-$\mathbf{b}$ plane (i.e. "C" in the space group symbol). But kplib library assumes the two-fold rotation to be along the $\mathbf{c}$-vector, or in other words, the $\mathbf{b}$-unique setting. A simple transformation is to just rotate the vectors like the following: $\mathbf{b} \rightarrow \mathbf{c}$, $\mathbf{c} \rightarrow \mathbf{a}$, and $\mathbf{a} \rightarrow \mathbf{b}$.

• For trigonal lattices, no matter in $hR$ or $hP$ representation, the conventional lattices should be primitive hexagonal lattices, i.e. the rhombohedrally-centered hexagonal lattice. This is the same as the international convention.

The algorithm doesn't put constraints on triclinic system, or on the centering type of orthorhombic lattices.

(Note: User could get the primary directions from the point symmetry opeartions.)

The C++ API

Class: KPointLatticeGenerator

template <typename T>
using Tensor = std::vector<std::vector<T>>;
/**
 * Constructor.
 *
 * To see how the variables are defined, check the "Conventions" section below)
 *
 * @param primVectorsArray            3x3 matrix with primitive lattice vectors in rows.
 * @param conventionalVectorsArray    3x3 matrix with conventional lattice vectors in rows.
 * @param latticePointOperatorsArray  point operators of the Laue Class
                                      of the input structure, expressed
                                      in the basis of primitive lattice vectors
 * @param numOperators                number of point operators in above array
 * @param isConventionalHexaognal     whether the conventional lattice is
                                      hexagonal
 */
KPointLatticeGenerator(const double primVectorsArray[][3],
                       const double conventionalVectorsArray[][3],
                       const int latticePointOperatorsArray[][3][3],
                       const int numOperators,
                       const bool isConventionalHexagonal);
/*
 * Specify whether to generate a gamma-centered grid or a shifted grid.
 * The available shifts are:
 *     {{0.0, 0.0, 0.5}, {0.0, 0.5, 0.0}, {0.5, 0.0, 0.0}, {0.5, 0.5, 0.0},
 *      {0.5, 0.0, 0.5}, {0.0, 0.5, 0.5}, {0.5, 0.5, 0.5}}
 * Basiclly, side centers, face centers and the body center.
 *
 * @param includeGamma   TRUE:  gamma-centered grid
 *                       FALSE: grid with one of the above shift
 *                       AUTO:  search both shifted and gamma-centered grid
 *                              and return the best one.
 */
enum INCLUDE_GAMMA { TRUE, FALSE, AUTO };
void includeGamma(INCLUDE_GAMMA includeGamma);
/*
 * @param minDistance  The returned grid should have a corresponding
 *                     real-space superlattice whose "minimum periodic distance"
 *                     is no smaller than this value.
 * @param minSize      Minimum number of total k-points of grids returned.
 */
KPointLattice getKPointLattice(const double minDistance,
                               const int minSize);

Class: KPointLattice

It's meant to hold the found k-point grid and provide query functions. The main query routines of this type:

double getMinPeriodicDistance();
int getNumDistinctKPoints();
int numTotalKPoints();

/*
 * @return Tensor<double> 2D arrays of coordinates. It's basically a wrapper
 *                        of "double coords[][3]".
 */
Tensor<double> getKPointCoordinates();

/*
 * @return vector<int>  1D array of k-points weights.
 */
std::vector<int> getKPointWeights();

Example: Using KpLib in C++ Code -- demo_kplib

To demonstrate the usage of the kpbib, we implemented a simple C++ application to generate optimized Generalised Monkhorst-Pack k-point grids. It use spglib to find symmetries (Togo and Tanaka, 2010) and output the k-point grid in the format of VASP KPOINTS file.

It is under the folder demo_kplib. We have included a pre-built binary of the spglib library. User can also choose to build the latest version of spglib from source and replace it. The executable demo_kplib can be built following these commands:

$ cd demo_kplib
$ mkdir build
$ cd build
$ cmake ..
$ make

The binary will be placed at ./build/demo_kplib. To call it, use:

$ ./demo_kplib /path/to/POSCAR /path/to/PRECALC > KPOINTS

The POSCAR is one of the standard VASP input file. The PRECALC file is the input file of demo_kplib and users can find its specifications on our website. Since it's for demonstration purposes, only the parameters MINDISTANCE, MINTOTALKPOINTS, and INCLUDEGAMMA are valid.

Examples of using this app for different crystal systems can be found in demo_kplib/examples. KPOINTS_ref are KPOINTS files generated by our server. KPOINTS_kplib are files created by the demo_kplib app.

For a stand-alone application with more funcnalities, like automatic detection of slabs, please check our K-point Grid Generator and K-point Server.

Code snippet of demo_kpib

Below are excerpts from the demo_kplib application to show how to use the KpLib API.

demo_kplib/src/main.cpp:

#include "kPointLatticeGenerator.h"
#include "utils.h"
#include "precalc.h"
#include "poscar.h"
#include <iostream>

int main(int argc, char **argv) {
    if (argc < 3) {
        std::cerr << "Usage: ./main /path/to/POSCAR /path/to/PRECALC"
                    << std::endl;
        return 1;
    }
    // Parse POSCAR and PRECALC.
    Poscar poscar;
    poscar.readFromPoscar(std::string(argv[1]));
    Precalc precalc(argv[2]);

    // Execute the main routines.
    KPointLatticeGenerator generator = initializeKPointLatticeGeneratorObject(
        poscar.primitiveLattice, poscar.coordinates, poscar.atomTypes);

    if (precalc.getIncludeGamma() == "TRUE") {
        generator.includeGamma(TRUE);
    } else if (precalc.getIncludeGamma() == "FALSE") {
        generator.includeGamma(FALSE);
    } else if (precalc.getIncludeGamma() == "AUTO") {
        generator.includeGamma(AUTO);
    }

    KPointLattice latticeGamma = generator.getKPointLattice(precalc.getMinDistance(),
                                                            precalc.getMinTotalKpoints());

    outputLattice(latticeGamma);
}

demo_kplib/src/utils.cpp:

#include "utils.h"
#include "spglib.h"
... // other includes and functions

// Wrapper of the KPointLatticeGenerator constructor.
KPointLatticeGenerator initializeKPointLatticeGeneratorObject(
        Tensor<double> primitiveLattice,
        Tensor<double> coordinates,
        std::vector<int> atomTypes) {

    double primLatticeArray[3][3] = {0};
    double conventionalLatticeArray[3][3] = {0};
    int rotation[192][3][3] = {0};
    int size = 0;
    bool isConventionalHexagonal = false;

    // use spglib to get necessary parameters for the consturctor
    // of KPointLatticeGenerator.
    ...

    KPointLatticeGenerator generator = KPointLatticeGenerator(primLatticeArray,
            conventionalLatticeArray, rotation, size, isConventionalHexagonal);
    if (useScaleFactor == "TRUE") {
        generator.useScaleFactor(spaceGroup); // Otherwise, use the fully dynamic search.
    }
    return generator;

}

Using scale factor to accelerate search

Scale factor is a scheme implemented to speed up the search of optimized generalized grids at the cost of returning a sub-optimal K-point grid. It is controlled by KPointsLatticeGenerator::useScaleFactor in the C++ library and the flag use_scale_factor in the Python interface.

When use_scale_factor is set to True in kpLib.interface::get_kpoints function or in the CLI command kpgen, the code will search exhaustively up to a pre-defined threshold of total number of K-points. If no grids are found with effective minimum distance at least min_distance, the user-given min_distance will be divide by n and then kplib search again for a grid satifying min_distance/n. If there are still no valid grids, kplib will increment n by 1 and search again until it find a valid grid. When a valid grid is finally found, the grid will be scaled up by n (i.e. total number of K-points is increased by n^3 times) before being returned to satisfy the min_distance requirement. Currently the maximum value of n is set to 3. The thresholds of maximum total number of K-points for exhaustive search are 729 (9x9x9) for triclinic structures, 1728 (12x12x12) for monoclinic structures, 5832 (18x18x18) for orthorhombic, tetragonal, trigonal and hexagonal structures, and 46656 (36x36x36) for cubic structures. User can increase the maximum value of n and these thresholds in KPointLatticeGenerator::useScaleFactor and recompile.

The C++ library turns on and off this scheme through the function KPointLatticeGenerator::useScaleFactor. It accepts the space group numbers of input structures to provide the flexibility of selectively using this scheme for a subset of user selected lattice types.