Chemical bonding in large systems using projected population analysis from real-space density functional theory calculations

TL;DR

This paper introduces a scalable computational method for projected population analysis in large-scale real-space DFT calculations, enabling detailed chemical bonding insights in extensive material systems with various boundary conditions.

Contribution

The work develops a unified, efficient framework within DFT-FE for projected population analysis directly on the FE grid, scalable to thousands of atoms and compatible with different boundary conditions.

Findings

Accurate chemical bonding analysis in large systems.

Scalable implementation on multi-node CPU architectures.

Successful benchmarking against established codes.

Abstract

We present an efficient and scalable computational approach for conducting projected population analysis from real-space finite-element (FE) based Kohn-Sham density functional theory calculations (DFT-FE). This work provides an important direction towards extracting chemical bonding information from large-scale DFT calculations on materials systems involving thousands of atoms while accommodating periodic, semi-periodic or fully non-periodic boundary conditions. Towards this, we derive the relevant mathematical expressions and develop efficient numerical implementation procedures that are scalable on multi-node CPU architectures to compute the projected overlap and Hamilton populations. The population analysis is accomplished by projecting either the self-consistently converged FE discretized Kohn-Sham orbitals, or the FE discretized Hamiltonian onto a subspace spanned by a localized atom-centred basis set. The proposed methods are implemented in a unified framework within DFT-FE code where the ground-state DFT calculations and the population analysis are performed on the same FE grid. We further benchmark the accuracy and performance of this approach on representative material systems involving periodic and non-periodic DFT calculations with LOBSTER, a widely used projected population analysis code. Finally, we discuss a case study demonstrating the advantages of our scalable approach to extract the quantitative chemical bonding information of hydrogen chemisorbed in large silicon nanoparticles alloyed with carbon, a candidate material for hydrogen storage.