Computational Design of Quantum Defects in Low-Dimensional Semiconductors

Defects are unavoidable entities in any material and disturb the behavior of a given pristine host crystal in one way or another. Depending on the application of interest, defects can either induce detrimental or advantageous property changes to the unperturbed system. The former might for example lead to defect induced reductions in the photovoltaic efficiencies or limitations due to back-scattering in transport applications whereas the latter can be a tool to create single-photon sources, intrinsically n- or p-type conducting materials, and qubits. Therefore, understanding the processes involved in the formation of defects is of utmost importance for the upcoming challenge of utilizing novel applications mitigated by defects. The field of two-dimensional (2D) materials represents a promising platform for investigations due to easier control of point defects as compared to their bulk counterparts.
In this thesis, we systematically study the impact of point defects on the pristine properties of two-dimensional materials by means of density functional theory. At the heart of our studies lies the evaluation of thermodynamic properties which gives fundamental insight into the stability and intrinsic behavior of a given defect system. We apply our methods to intrinsic and extrinsic defect entities, such as vacancies, substitutional defects, interstitials, and adatoms to understand the limitations posed by disruptions to various 2D host crystals. From a computational point of view, we develop high-throughput functionality for defect calculations in materials science to automatically set up and characterize a large number of defect systems simultaneously.
As a starting point, for the specific host material of 2H-MoS₂, we use first-principle calculations as a useful tool to identify different intrinsic defect types in experimentally grown MoS₂ samples and establish the importance of vacancy, antisite, and grain boundary defects in real systems. We then build the bridge to a detailed property characterization of 500 simple intrinsic defects within a host crystal set of over 80 two-dimensional semiconductors. Our results give valuable insight into the formation processes of vacancy and antisite defects, analyze the nature of occurring defect states in the pristine band gap, and quantify intrinisic dopabilities as well as defect tolerances. The results are published in the open-access Quantum Point Defects in 2D Materials (QPOD) database. Subsequently, we turn our attention to extrinsic doping (via the incorporation of interstitial defects and adatoms) in experimentally known monolayer materials. We address the question whether a given dopant atom within a specific host material favors adsorption over absorption or vice versa and find out that doping 2D materials in interstitial positions is a generally challenging task. Lastly, we briefly discuss our contribution to two joint efforts of the CAMD section, namely the recent progress of the Computational 2D Materials Database (C2DB) and the Atomic Simulation Recipes (ASR), a Python framework for automated workflows.
Overall, the obtained results stress the importance of defects in any in-depth characterization of semiconducting host materials, build knowledge towards understanding the fundamental formation processes for defects in monolayer materials, and identify trends that should be factored into the planning for upcoming studies. The computational frameworks developed within this thesis can be used as the building blocks for future investigations within the field of point defects in two-dimensional materials. The publication of open-access datasets focusing on point defects, e.g. the QPOD database, is a valuable resource for subsequent machine learning studies which could help in circumventing the computationally demanding task of supercell defect calculations by means of accurate first principle methods for screening purposes.