Abstract

This paper presents a comprehensive update to PyProcar, a versatile Python package for analyzing and visualizing density functional theory (DFT) calculations in materials science. The latest version introduces a modularized codebase, a centralized example data repository, and a robust testing framework, offering a more reliable, maintainable, and scalable platform. Expanded support for various DFT codes broadens its applicability across research environments. Enhanced documentation and an example gallery make the package more accessible to new and experienced users. Incorporating advanced features such as band unfolding, noncollinear calculations, and derivative calculations of band energies enriches its analytic capabilities, providing deeper insights into electronic and structural properties. The package also incorporates PyPoscar, a specialized toolkit for manipulating POSCAR files, broadening its utility in computational materials science. These advancements solidify PyProcar's position as a comprehensive and highly adaptable tool, effectively serving the evolving needs of the materials science community. New version program summary Program title: PyProcar CPC Library link to program files: https://doi .org /10 .17632 /d4rrfy3dy4 .2 Developer's repository link: https://github .com /romerogroup /pyprocar Licensing provisions: GPLv3 Programming language: Python Supplementary material: Pyprocar-Supplementary Information Journal reference of previous version: Comput. Phys. Commun. 251 (2020) 107080, https://doi .org /10 .1016 /j . cpc .2019 .107080 Does the new version supersede the previous version?: Yes Reasons for the new version: Changes in the directory structure, the addition of new features, enhancement of the manual and user documentation, and generation of interfaces with other electronic structure packages. Summary of revisions: These updates enhance its capabilities and ensure developers' and users' maintainability, reliability, and ease of use. Nature of problem: To automate, simplify, and serialize the analysis of band structure and Fermi surface, especially for high throughput calculations. Solution method: Implement a Python library able to handle, combine, parse, extract, plot, and even repair data from density functional calculations from diverse electronic structure packages. PyProcar uses color maps on the band structures or Fermi surfaces to give a simple representation of the relevant characteristics of the electronic structure. Additional comments including restrictions and unusual features: PyProcar can produce high-quality figures of band structures and Fermi surfaces (2D and 3D), projection of atomic orbitals, atoms, and/or spin components.