Benchmarking First-Principles Approaches for the Band Gap Prediction of Nanoporous Materials

We present a comprehensive benchmarking study of first-principles calculation methods, based on density functional theory (DFT) and its extensions, to evaluate the fundamental and optical band gaps of nanoporous materials, including metal-organic frameworks (MOFs), covalent-organic frameworks (COFs), and a zeolite. We find that a hybrid approach using the HSE06 functional generally underestimates the fundamental band gaps compared to the nonself-consistent GW (G<sub>0</sub>W<sub>0</sub>) approximation, and a DFT approach incorporating self-consistent extended Hubbard interactions shows varying agreement with G<sub>0</sub>W<sub>0</sub> results depending on the electronic characteristics of materials. Using the Bethe-Salpeter equation (BSE) on top of G<sub>0</sub>W<sub>0</sub> calculations (G<sub>0</sub>W<sub>0</sub>+BSE) and time-dependent DFT (TDDFT) with the PBE functional, we compute optical band gaps and absorption spectra that are in good agreement with experiments. In particular, G<sub>0</sub>W<sub>0</sub>+BSE outperforms TDDFT with mean absolute errors (MAEs) of 0.68 and 1.00 eV, respectively, for the computed optical band gaps. Furthermore, we find that exciton binding energies in nanoporous materials are significantly larger than those of inorganic systems, attributed to the spatial localization of the valence band maximum (VBM) and conduction band minimum (CBM) on the same structural subunit. These results provide valuable insights into the performance of different computational methodologies for nanoporous materials and offer practical guidance for the development and application of first-principles approaches in materials with high porosity.

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Publication Details

Journal
Journal of Chemical Theory and Computation
Published
2025-06-30
DOI
https://doi.org/10.1021/acs.jctc.5c00818
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
0.00

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article

Benchmarking First-Principles Approaches for the Band Gap Prediction of Nanoporous Materials

Young‐Woo Son, Sanghoon Lee, Jung‐Hoon Lee
Journal of Chemical Theory and Computation
Machine Learning in Materials Science
article

Benchmarking First-Principles Approaches for the Band Gap Prediction of Nanoporous Materials

Young‐Woo Son, Sanghoon Lee, Jung‐Hoon Lee
article en

Abstract

We present a comprehensive benchmarking study of first-principles calculation methods, based on density functional theory (DFT) and its extensions, to evaluate the fundamental and optical band gaps of nanoporous materials, including metal-organic frameworks (MOFs), covalent-organic frameworks (COFs), and a zeolite. We find that a hybrid approach using the HSE06 functional generally underestimates the fundamental band gaps compared to the nonself-consistent GW (G<sub>0</sub>W<sub>0</sub>) approximation, and a DFT approach incorporating self-consistent extended Hubbard interactions shows varying agreement with G<sub>0</sub>W<sub>0</sub> results depending on the electronic characteristics of materials. Using the Bethe-Salpeter equation (BSE) on top of G<sub>0</sub>W<sub>0</sub> calculations (G<sub>0</sub>W<sub>0</sub>+BSE) and time-dependent DFT (TDDFT) with the PBE functional, we compute optical band gaps and absorption spectra that are in good agreement with experiments. In particular, G<sub>0</sub>W<sub>0</sub>+BSE outperforms TDDFT with mean absolute errors (MAEs) of 0.68 and 1.00 eV, respectively, for the computed optical band gaps. Furthermore, we find that exciton binding energies in nanoporous materials are significantly larger than those of inorganic systems, attributed to the spatial localization of the valence band maximum (VBM) and conduction band minimum (CBM) on the same structural subunit. These results provide valuable insights into the performance of different computational methodologies for nanoporous materials and offer practical guidance for the development and application of first-principles approaches in materials with high porosity.

Journal of Chemical Theory and ComputationVol. 21(14)
Korea Institute for Advanced Study (KR), Beijing Computational Science Research Center (CN), PRG S&Tech (South Korea), Korea Institute of Science and Technology (KR)
Korea Institute of Science and Technology, National Supercomputing Center, Korea Institute of Science and Technology Information
Openalex Percentile: Top 89%
Machine Learning in Materials Science
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