What question did this study set out to answer?

This research aims to improve electronic bandgap predictions by reparameterizing the SCAN functional in density functional theory.

March 10, 2026Open Access

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

Key Points

This research aims to improve electronic bandgap predictions by reparameterizing the SCAN functional in density functional theory.
Propose system dependent functional (SD-SCAN) with adjusted parameters for SCAN
Evaluate bandgap predictions against experimental values across materials
Develop machine learning model (ML-SCAN) to predict SCAN parameters from solid-state descriptors
Create a simplified variant (SCAN-0.2) for easier bandgap calculations
SD-SCAN yields bandgaps closer to experimental values for most covalent materials
Improvements for highly ionic systems remain limited due to SCAN’s α-dependence
Enhanced exchange in covalent regions raises conduction bands and broadens charge density
ML-SCAN offers a competitive, flexible strategy for reparametrization of SCAN functionals

Abstract

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.

Demander à l'IA

Bookmark

View Full Paper

Cite This Study

Dovale-Farelo et al. (Sat,) studied this question.

synapsesocial.com/papers/69af955970916d39fea4cd2c https://doi.org/https://doi.org/10.1038/s41524-026-02009-w

Demander à l'IA

Bookmark

View Full Paper