Benchmarking Density Functional Theory for Accurate Calculation of Nitride Band Gaps

TL;DR

This paper evaluates various computational methods for predicting the electronic properties of nitride materials, finding that some advanced functionals significantly outperform traditional ones.

Contribution

The study introduces a new benchmark dataset of nitride materials and evaluates the accuracy of multiple exchange-correlation functionals for band gap calculations.

Findings

Conventional LDA/PBE functionals significantly underestimate band gaps with large errors.

Advanced functionals like mBJ and HSE06 show high accuracy with low mean absolute errors.

The dataset includes diverse nitrides to support future large-scale material screening.

Abstract

We benchmark exchange-correlation functionals for the
calculation
of fundamental band gaps of inorganic nitrides. These include conventional
functionals such as the local density approximation (LDA), the generalized-gradient
(Perdew–Burke–Ernzerhof) approximation (PBE), simple
Slater exchange functionals (SLOC), specialized LDA/GGA-derived high
local exchange (HLE16) and Armiento–Kümmel semilocal
(AK13) functionals, meta-GGA functionals including TASK, the modified
Becke–Johnson functional (mBJ), and Heyd–Scuseria–Ernzerhof
(HSE06) hybrid functional, as well as quasiparticle GW theory. Since inorganic nitrides remain strongly under-represented
in previous extensive benchmark studies, the current subdatabase contributes
towards building a future large-scale balanced materials compilation
of band gaps to benchmark theory. From a literature survey, we carefully
collect 25 binary and 11 ternary nitrides with a focus on semiconductors
spanning the periodic table, including ionic Li3N, antibixbyite-structured
X3N2 (X = Be, Mg, Ca), early transition metals
and lanthanides (e.g., ScN, YN, and LaN), ultrahard Th3P4-type structured M3N4 (M = Zr,
Hf) compounds, promising photocatalysts Ta3N5, different polymorphs of III–V reference covalent nitrides
(BN, AlN, GaN), and many M3N4 polymorphs (M
= C, Si, and Ge) such as spinel-structured phases. Consistent with
previous extensive benchmark tests, conventional LDA/PBE unsystematically
largely underestimate band gaps with mean absolute errors (MAE) of
>1.0 eV and mean absolute percentage errors (MAPE) of about 50%.
Simple
Slater exchange functional, SLOC, the GGA-derived AK13LDA and HLE16
functionals show improvement over LDA/PBE with MAE of 0.5–0.6
eV (MAPE ∼ 20–25%) with mBJ and HSE06 being the most
accurate, with MAE = 0.30 and 0.28 eV (MAPE 12.1% and 11.1%), respectively.
Strategies for the development of machine learning and the choice
of appropriate exchange-correlation functionals for high-throughput
large-scale material screening are discussed in light of these results.

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