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Computational Discovery of Two-dimensional Materials: The discovery of materials with unprecedented properties has historically underpinned technological revolutions, from the advent of metallurgy to the invention of the transistor at AT&T Bell Laboratories in 1947. For much of the 20th century, progress relied predominantly on experimental trial-and-error, where validation of structure, stability, and properties occurred iteratively. While effective, this route was inherently time-consuming, often requiring years to translate a proof-of-concept into an application-ready technology.

In the last two decades, advances in high-performance computing (HPC) and algorithm development have transformed this paradigm. Computational prediction now constitutes an essential component of condensed matter physics and materials science, complementing and in many cases guiding experimental discovery. Central to this approach are first-principles (ab initio) calculations, which require no empirical input. High-throughput frameworks employ these methods to screen thousands of candidate materials, both experimentally realized and hypothetical, for their structural, electronic, magnetic, and thermal characteristics. Thermodynamic stability is typically assessed using convex hull analysis, while subsequent constraints tailored to specific functionalities (e.g., valleytronics, thermoelectrics, or catalysis) further refine the candidate pool. This computational-aided discovery pipeline dramatically reduces the time between conceptualization and application by identifying viable material platforms prior to synthesis. As such, it accelerates the design of van der Waals heterostructures, 2D materials, and quantum materials with transformative potential for electronics, sustainable energy, and emerging quantum technologies.

2D materials and heterostructures for Valleytronics: Analogous to the role of charge in conventional electronics, the valley degree of freedom provides a binary state variable in the emerging field of valleytronics, offering significant advantages for ultrafast data processing. In this framework, electrons carry a pseudospin associated with their crystal momentum and valley index. The large momentum separation between inequivalent valleys suppresses inter-valley scattering, enabling robust pseudospin states that are promising for room-temperature valley-based quantum information processing and communication technologies. A central challenge in valleytronics is the control of valley pseudospin, as valley states couple only weakly to external electric and magnetic fields. The discovery of two-dimensional (2D) transition metal dichalcogenides (TMDs) has opened new pathways to address this challenge. In these systems, valley degeneracy can be lifted by breaking time-reversal symmetry, thereby enabling selective valley polarization.

One of the most promising strategies involves achieving permanent valley polarization through magnetic proximity effects, placing TMDs in contact with layered magnetic materials that induce symmetry breaking. However, a key bottleneck remains: identifying and engineering suitable magnetic monolayers that can provide sufficiently strong valley polarization while maintaining stability and compatibility with 2D heterostructures. Advances in computational prediction and materials design are accelerating the search for such candidates, with the ultimate goal of realizing practical valleytronic devices for next-generation quantum and optoelectronic technologies. 

Materials for Catalytic Reactions: Catalytic reactions such as nitrogen reduction to ammonia (NRR) and COâ‚‚ reduction to value-added chemicals (COâ‚‚RR) are among the most critical processes for sustainable energy and food security. Ammonia is the cornerstone of fertilizer production, supporting global agriculture and food supply chains. In the United Arab Emirates, the Abu Dhabi National Oil Company (ADNOC) has recently announced the construction of a world-scale ammonia production facility in Ruwais, reinforcing the UAE’s position as a leader in hydrogen and ammonia-based value chains while contributing to both economic diversification and the global energy transition.

Traditionally, precious-metal catalysts such as Pt, Pd, Ir, and Ru have served as benchmarks for key electrochemical reactions, including the hydrogen evolution reaction (HER), oxygen evolution reaction (OER), NRR, and COâ‚‚RR. However, their scarcity and high cost impose serious limitations on large-scale deployment. To overcome these challenges, our research group explores alternative low-cost catalytic materials, focusing particularly on two-dimensional (2D) electrocatalysts. Owing to their large surface-to-volume ratio and tunable electronic properties, 2D materials can significantly reduce energy barriers, enhance charge transfer kinetics, and improve electrochemical efficiency.

Despite these advantages, the field of 2D electrocatalysis remains at an early stage, and further progress is required to achieve industrial-scale feasibility. Our work employs advanced computational design and high-throughput screening to identify promising candidates capable of replacing precious metals, with the ultimate goal of enabling scalable, sustainable, and cost-effective catalytic technologies for energy and environmental applications.

2D Materials for Thermoelectrics: Advances in thermoelectric (TE) technology offer a pathway to reduce dependence on fossil fuels by converting waste heat into usable electricity and integrating green resources into the national energy mix. Thermoelectric generators (TEGs), which harvest energy from industrial, automotive, and even body heat, represent a promising solution to the global energy challenge by simultaneously improving energy efficiency and contributing to clean energy production. The development of high-performance TEGs, however, critically depends on the discovery of materials with strong thermoelectric responses. The thermoelectric figure of merit (ZT) can be optimized by carefully tuning a material’s electrical conductivity, Seebeck coefficient, and thermal conductivity. This balance is particularly challenging, as these parameters are often interdependent.

Our research focuses on the design and prediction of two-dimensional (2D) materials for thermoelectrics. Due to their reduced dimensionality, quantum confinement, and highly tunable electronic structures, 2D systems offer unique opportunities to decouple electrical and thermal transport, thereby achieving enhanced ZT values. By leveraging first-principles calculations and high-throughput computational screening, our group identifies novel candidates with superior thermoelectric efficiency and stability. This research direction directly supports the goals of the UAE Energy Strategy 2050, which aims for 44% of the nation’s energy mix to come from clean and renewable sources. Developing efficient thermoelectric materials not only advances sustainable energy technologies but also strengthens the UAE’s leadership in the global clean energy transition.