Search for: All records

Defects have a profound impact on the electronic and physical properties of crystals. For two-dimensional (2D) materials, many intrinsic point defects have been reported, but much remains to be understood about their origin. Using scanning transmission electron microscopy imaging, this study discovers various linear arrays of W-vacancy defects that are explained in the context of the crystal growth of coalesced, monolayer WS2. Atomistic-scale simulations show that vacancy arrays can result from steric hindrance of bulky gas-phase precursors at narrowly separated growth edges, and that increasing the edge separation leads to various intact and defective growth modes, which are driven by competition between the catalytic effects of the sapphire substrate and neighboring growth edge. Therefore, we hypothesize that the arrays result from combined growth modes, which directly result from film coalescence. The connections drawn here will guide future synthetic and processing strategies to harness the engineering potential of defects in 2D monolayers. 

The successful discovery and isolation of graphene in 2004, and the subsequent synthesis of layered semiconductors and heterostructures beyond graphene have led to the exploding field of two-dimensional (2D) materials that explore their growth, new atomic-scale physics, and potential device applications. This review aims to provide an overview of theoretical, computational, and machine learning methods and tools at multiple length and time scales, and discuss how they can be utilized to assist/guide the design and synthesis of 2D materials beyond graphene. We focus on three methods at different length and time scales as follows: (i) nanoscale atomistic simulations including density functional theory (DFT) calculations and molecular dynamics simulations employing empirical and reactive interatomic potentials; (ii) mesoscale methods such as phase-field method; and (iii) macroscale continuum approaches by coupling thermal and chemical transport equations. We discuss how machine learning can be combined with computation and experiments to understand the correlations between structures and properties of 2D materials, and to guide the discovery of new 2D materials. We will also provide an outlook for the applications of computational approaches to 2D materials synthesis and growth in general.

 

Ferroelectric materials such as barium titanate (BaTiO3) have a wide range of applications in nano scale electronic devices due to their outstanding properties. In this study, we developed an easily extendable atomistic ReaxFF reactive force field for BaTiO3 that can capture both its field- as well as temperature-induced ferroelectric hysteresis and corresponding changes due to surface chemistry and bulk defects. Using our force field, we were able to reproduce and explain a number of experimental observations: (1) existence of a critical thickness of 4.8 nm below which ferroelectricity vanishes in BaTiO3; (2) migration and clustering of oxygen vacancies (OVs) in BaTiO3 and reduction in the polarization and the curie temperature due to the OVs; (3) domain wall interaction with surface chemistry to influence ferroelectric switching and polarization magnitude. This new computational tool opens up a wide range of possibilities for making predictions for realistic ferroelectric interfaces in energy-conversion, electronic and neuromorphic systems.