Skip to main content
Join the official Python Developers Survey 2018 and win valuable prizes: Start the survey!

Parallel C/Python package for numerical analysis of PAW DFT wavefunctions

Project description

PAWpySeed

https://kylebystrom.github.io/pawpyseed/

WARNING: PAWpySeed is still in early development. Documentation is incomplete, and some features are not yet thoroughly tested. High-priority issues for the next couple months include: 1) documentation page containing the mathematical formalism, 2) examples and tutorials, 3) standardized unit tests. The evaluation of overlap operators is tested, but a standard test suite is not yet published, and some features still require more thorough testing.

PAWpySeed is a parallelized Python and C tool for reading and analyzing the optimized band structure and wave functions of VASP DFT calculations. The code is written for the PAW formalism developed by P.E. Blochl and implemented in VASP.

Installation

Note: Before installing with pip or the setup script, please read about the dependencies and set up your environment correctly!

You can install PAWpySeed with pip.

pip install pawpyseed

This has been tested on Scientific Linux 7 and Linux Mint 18, but should work for systems that have the appropriate packages and environment variables defined as described below. Note that the setup script compiles and installs some C code as a shared object to be used by ctypes and places it in the package directory under pawpyseed/core/.

Installation can also be performed by cloning this repository and running the setup.py script in the root directory of the repository.

python setup.py build
python setup.py install

The build command, in addition to the standard distutils setup, compiles the C code in the pawpyseed.core module into a shared object in the core module, pawpy.so. See below for dependency details.

Dependencies

All dependencies indicate the minimum version tested. PAWpySeed might work fine with earlier versions, but use of older versions will not be officially supported.

Python requirements:

python>=3.5
numpy>=1.14
scipy>=1.0
pymatgen>=2018.2.13

C requirements:

icc >= 16.0.4 OR gcc >= 4.8.5
Intel Math Kernel Library >= 11.3.4

Intel MKL is available for free installation on a variety of platforms. Most computing clusters will have Intel MKL, and you can install it on your desktop (or any system to which you have root access) by following the relevant instructions at the following URL: https://software.intel.com/en-us/articles/free-ipsxe-tools-and-libraries. After Intel MKL is installed, add the following line to your .bashrc to link MKL (NOTE: this might not be the exact directory that MKL is in, you need to check that first):

export MKLROOT=/opt/intel/compilers_and_libraries_2018/linux/mkl
export LD_LIBRARY_PATH=$MKLROOT/lib/intel64_lin:$LD_LIBRARY_PATH
export C_INCLUDE_PATH=$MKLROOT/include:$C_INCLUDE_PATH

The last line is optional but might be useful for future PAWpySeed builds and other programs which make use of MKL. The setup.py file will now take care of C compilation.

Optional Python dependencies (not used now but might be in the future):

sympy>=1.1.1
matplotlib>=0.2.5

Theory and Input

PAW

The projector augmented wave (PAW) method is a technique used in plane wave density functional theory to simplify the description of the wavefunctions near the nuclei of a system. The strong Coulombic forces near an atomic nucleus creates quickly oscillating wavefunctions that are not well described by plane waves without prohibitively large basis sets, so a "pseudopotential" is introduced near the atomic nuclei which results in smooth "pseudowavefunctions" well described by plane waves. The full wavefunctions can be recovered by a linear transform of the pseudowavefunctions. The PAW method requires three sets of functions: projector functions, onto which pseudowavefunctions are projected to probe their character; full partial waves, which describe atomic valence states derived from the true potential; and pseudo partial waves, which are derived from the full partial waves and pseudopotential.

Files

The projector functions and partial waves are unique to each element and stored in the POTCAR file used in a VASP calculation. The pseudowavefunction is the part of the wavefunction optimized during a DFT calculation and is stored in the WAVECAR output file in VASP. PAWpySeed parses both files to retrieve all parts of the full Kohn Sham wavefunctions.

The Code

The main purpose of PAWpySeed is to evaluate overlap operators between Kohn-Sham wavefunctions from different structures, which is not done by standard plane-wave DFT codes. Such functionality can be useful for analyzing the composition of defect levels in solids, which is main application for which the code is currently focused.

Implementation

  • Python Interface
  • Computationally intensive tasks in C
  • Parallelized with openmp

Current Functionality

  • Read pseudowavefunctions
  • Read projectors and partial waves from VASP POTCAR
  • Evaluate overlap operators between bands, including when bands belong to different structures with the same lattice
  • Project point defect levels onto bulk valence and conduction bands
  • Convenient pycdt interface

Future Functionality

  • Localize orbitals with SCDM-k
  • Atomic Hartree Fock and GGA DFT database for use in charge corrections and other applications
  • Read noncollinear pseudowavefunctions
  • Convert PAW wavefunctions to NC wavefunctions (for use in GW calculations)
  • Perturbative charge corrections
  • Read pseudopotential, atomic charge density, and other POTCAR data
  • Perform general operator expectation values on full wavefunctions

Acknowledgments

The code in PAWpySeed is based on a several algorithms and codes, which are enumerated here.

  1. PAW: The PAW method was developed by P. E. Blochl in 1994. His paper deriving the method was helpful to me in deriving the extensions to the formalism needed to develop this code.
    • P. E. Blochl. Projector augmented-wave method. Phys. Rev. B, 50:17953, 1994.
  2. VASP: PAWpySeed is primarily built to read and process the output of VASP calculations. PAWpySeed reads PAW wavefunctions and calculate overlap operators using algorithms derived from VASP and other plane-wave codes, so the following citations are necessary. The last citation is specifically for the PAW method and potentials. See the VASP website at http://community.hartree.stfc.ac.uk/wiki/site/admin/vasp.html for information on citing specific functionals.
    • G. Kresse and J. Hafner. Ab initio molecular dynamics for liquid metals. Phys. Rev. B, 47:558, 1993.
    • G. Kresse and J. Hafner. Ab initio molecular-dynamics simulation of the liquid-metal-amorphous-semiconductor transition in germanium. Phys. Rev. B, 49:14251, 1994.
    • G. Kresse and J. Furthmüller. Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set. Comput. Mat. Sci., 6:15, 1996.
    • G. Kresse and J. Furthmüller. Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set. Phys. Rev. B, 54:11169, 1996.
    • G. Kresse and D. Joubert. From ultrasoft pseudopotentials to the projector augmented-wave method. Phys. Rev. B, 59:1758, 1999.
  3. NUMSBT: NUMSBT is a code written by J. D. Talman, which implements an algorithm that calculates the spherical Bessel transform (SBT) in O(NlogN) time. PAWpySeed employs the high-k transform algorithm implemented in NUMSBT to calculate the overlap operators between overlapping augmentation spheres that have different positions or elements. It is also used to filter out high-frequency components from AE partial waves, which allows projections from pseudowavefunctions to AE partial waves to be performed in real space, which is a vital component of the code. NUMSBT is distributed under the Standard CPC License, and the algorithm is developed in the following paper:
    • Talman, J. Computer Physics Communications 2009, 180, 332 –338.
  4. Doxygen: Doxygen is a documentation generator from which I built the docs for PAWpySeed. It is an excellent tool that allows for clean, up-to-date documentaton that is easy to make and navigate. Check it out at http://www.stack.nl/~dimitri/doxygen/

Questions and Comments

Find a bug? Areas of code unclearly documented? Other questions? Feel free to contact Kyle Bystrom at kylebystrom@berkeley.edu with the subject "pawpyseed: <Topic>".

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Filename, size & hash SHA256 hash help File type Python version Upload date
pawpyseed-0.0.4a0.tar.gz (732.7 kB) Copy SHA256 hash SHA256 Source None Aug 20, 2018

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN SignalFx SignalFx Supporter DigiCert DigiCert EV certificate StatusPage StatusPage Status page