Advanced modeling of materials with PAOFLOW 2.0: New features and software design

TL;DR

PAOFLOW 2.0 is a redesigned, Python-based software tool that enhances the modeling of quantum materials by adding new features like symmetry operations, atomic orbital projections, and improved performance for property predictions.

Contribution

The paper introduces a comprehensive redesign of PAOFLOW, including new features such as symmetry operations, atomic orbital projections, and a Python API, significantly improving its capabilities and usability.

Findings

Reduced runtime for k-point unfolding due to symmetry operations

Enabled generation of real space atomic orbitals

Supported models with non-constant relaxation times

Abstract

Recent research in materials science opens exciting perspectives to design novel quantum materials and devices, but it calls for quantitative predictions of properties which are not accessible in standard first principles packages. PAOFLOW is a software tool that constructs tight-binding Hamiltonians from self-consistent electronic wavefunctions by projecting onto a set of atomic orbitals. The electronic structure provides numerous materials properties that otherwise would have to be calculated via phenomenological models. In this paper, we describe recent re-design of the code as well as the new features and improvements in performance. In particular, we have implemented symmetry operations for unfolding equivalent k-points, which drastically reduces the runtime requirements of first principles calculations, and we have provided internal routines of projections onto atomic orbitals enabling generation of real space atomic orbitals. Moreover, we have included models for non-constant relaxation time in electronic transport calculations, doubling the real space dimensions of the Hamiltonian as well as the construction of Hamiltonians directly from analytical models. Importantly, PAOFLOW has been now converted into a Python package, and is streamlined for use directly within other Python codes. The new object oriented design treats PAOFLOWs computational routines as class methods, providing an API for explicit control of each calculation.