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<p>This work investigates how reparametrizing the Strongly Constrained and Appropriately Normed (SCAN) exchange-correlation (XC) functional within density functional theory affects predictions of the electronic bandgap (<i>E</i><sub><i>g</i></sub>) for solids. A system dependent functional (SD-SCAN) is proposed by adjusting a subset of SCAN’s internal parameters to improve bandgaps. For most covalent materials, SD-SCAN yields bandgaps closer to experimental values while preserving accurate lattice constants; improvements remain limited for highly ionic systems, reflecting constraints of SCAN’s <i>α</i>-dependence and the absence of long-range nonlocal (Hartree-Fock-like) exchange at the semilocal/meta-GGA level. The modified parameters enhance exchange in regions with covalent character, raising the conduction bands and broadening the charge density, thereby yielding more realistic electronic structures and improved dielectric response. A machine-learning model (ML-SCAN) predicts SCAN parameters from solid-state descriptors, providing a flexible, system-dependent reparametrization strategy competitive with existing semilocal approaches. A simplified variant, SCAN-0.2, offers a fixed-parameter shortcut for improved bandgap calculations. Overall, this study lays the groundwork for ML-driven XC functionals for semiconductors.</p>

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System-conditioned reparameterization of the SCAN functional for accurate bandgaps: from analytical constraints to machine learning

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月7日

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  • Viviana Dovale-Farelo,
  • Pedram Tavadze,
  • Miguel A. L. Marques,
  • Srinjoy Das,
  • Kamal Choudhary,
  • Alejandro Bautista-Hernández,
  • Aldo H. Romero

摘要

This work investigates how reparametrizing the Strongly Constrained and Appropriately Normed (SCAN) exchange-correlation (XC) functional within density functional theory affects predictions of the electronic bandgap (Eg) for solids. A system dependent functional (SD-SCAN) is proposed by adjusting a subset of SCAN’s internal parameters to improve bandgaps. For most covalent materials, SD-SCAN yields bandgaps closer to experimental values while preserving accurate lattice constants; improvements remain limited for highly ionic systems, reflecting constraints of SCAN’s α-dependence and the absence of long-range nonlocal (Hartree-Fock-like) exchange at the semilocal/meta-GGA level. The modified parameters enhance exchange in regions with covalent character, raising the conduction bands and broadening the charge density, thereby yielding more realistic electronic structures and improved dielectric response. A machine-learning model (ML-SCAN) predicts SCAN parameters from solid-state descriptors, providing a flexible, system-dependent reparametrization strategy competitive with existing semilocal approaches. A simplified variant, SCAN-0.2, offers a fixed-parameter shortcut for improved bandgap calculations. Overall, this study lays the groundwork for ML-driven XC functionals for semiconductors.