Electronic Structure of Low‐Dimensional Inorganic/Organic Interfaces: Hybrid Density Functional Theory, G0W0, and Electrostatic Models

• Published in: Physica status solidi. A, Applications and materials science Vol. 221; no. 1
• Main Authors: Krumland, Jannis, Cocchi, Caterina
• Format: Journal Article
• Language: English
• Published: Weinheim Wiley Subscription Services, Inc 01.01.2024
• Subjects: Accuracy; Anthracene; Bulk density; Computing costs; Density functional theory; Electronic properties; Electronic structure; Elementary excitations; Environment models; First principles; Hybrid systems; Molybdenum disulfide; Parameterization; Perturbation theory; Substrates
• ISSN: 1862-6300; 1862-6319
• DOI: 10.1002/pssa.202300089

First‐principles simulations of electronic properties of hybrid inorganic/organic interfaces are challenging, as common density functional theory (DFT) approximations target specific material classes like bulk semiconductors or gas‐phase molecules. Taking as a prototypical example anthracene (ANT) physisorbed on monolayer MoS2, the ability of different ab initio schemes to describe the electronic structure using semilocal and hybrid DFT is assessed. For the latter, an unconstrained three‐parameter range‐separation scheme is used. Comparisons against results from the many‐body perturbation theory indicate that properly parametrized hybrid functionals can approximate with reasonable accuracy the quasiparticle properties of both ANT and MoS2 taken by themselves. However, this is not the case for the hybrid interface, where neither functional can predict the correct‐level alignment nor provide a particularly good starting point for G0W0 calculations. It is shown that nonempirically parametrized electrostatic models can accomplish the same task at negligible computational costs. Such schemes can include substrates of hybrid interfaces in good agreement with experimental data. The results indicate that currently, fully atomistic, many‐body simulations of weakly interacting hybrid systems are not worth the required computational resources. In contrast, ab initio‐parametrized effective models mimicking the environment offer a scalable alternative without compromising accuracy and predictivity.