Search for: All records

High entropy oxides are emerging as an exciting new avenue to design highly tailored functional behaviors that have no traditional counterparts. Study and application of these materials are bringing together scientists and engineers from physics, chemistry, and materials science. The diversity of each of these disciplines comes with perspectives and jargon that may be confusing to those outside of the individual fields, which can result in miscommunication of important aspects of research. In this Perspective, we provide examples of research and characterization taken from these different fields to provide a framework for classifying the differences between compositionally complex oxides, high entropy oxides, and entropy stabilized oxides, which is intended to bring a common language to this emerging area. We highlight the critical importance of understanding a material’s crystallinity, composition, and mixing length scales in determining its true definition. 

Stacking layers of atomically thin transition-metal carbides and two-dimensional (2D) semiconducting transition-metal dichalcogenides, could lead to nontrivial superconductivity and other unprecedented phenomena yet to be studied. In this work, superconducting α-phase thin molybdenum carbide flakes were first synthesized, and a subsequent sulfurization treatment induced the formation of vertical heterolayer systems consisting of different phases of molybdenum carbide—ranging from α to γ′ and γ phases—in conjunction with molybdenum sulfide layers. These transition-metal carbide/disulfide heterostructures exhibited critical superconducting temperatures as high as 6 K, higher than that of the starting single-phased α-Mo 2 C (4 K). We analyzed possible interface configurations to explain the observed moiré patterns resulting from the vertical heterostacks. Our density-functional theory (DFT) calculations indicate that epitaxial strain and moiré patterns lead to a higher interfacial density of states, which favors superconductivity. Such engineered heterostructures might allow the coupling of superconductivity to the topologically nontrivial surface states featured by transition-metal carbide phases composing these heterostructures potentially leading to unconventional superconductivity. Moreover, we envisage that our approach could also be generalized to other metal carbide and nitride systems that could exhibit high-temperature superconductivity. 

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.