Ramakrishnan, K and Nori, SKK and Lee, S-C and Das, GP and Bhattacharjee, S and Motamarri, P (2023) Chemical Bonding in Large Systems Using Projected Population Analysis from Real-Space Density Functional Theory Calculations. In: Journal of Chemical Theory and Computation, 19 (13). pp. 4216-4231.
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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 toward extracting chemical bonding information from large-scale DFT calculations on materials systems involving thousands of atoms while accommodating periodic, semiperiodic, or fully nonperiodic boundary conditions. Toward this, we derive the relevant mathematical expressions and develop efficient numerical implementation procedures that are scalable on multinode 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-centered basis set. The proposed methods are implemented in a unified framework within the 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 nonperiodic 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. © 2023 American Chemical Society.
| Item Type: | Journal Article |
|---|---|
| Publication: | Journal of Chemical Theory and Computation |
| Publisher: | American Chemical Society |
| Additional Information: | The copyright for this article belongs to American Chemical Society. |
| Department/Centre: | Division of Interdisciplinary Sciences > Computational and Data Sciences |
| Date Deposited: | 22 Aug 2024 09:59 |
| Last Modified: | 22 Aug 2024 09:59 |
| URI: | http://eprints.iisc.ac.in/id/eprint/85557 |
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