Two-dimensional (2D) transitional metal-based carbides or nitrides, commonly known as MXenes, represent an emerging class of 2D materials with remarkable functional properties and performance since their discovery in 2011. While extensive experimental and computational studies have investigated the structures and properties of 2D MXenes, most studies have primarily focused on compositions with one or two transition metals. Inspired by the rapid development of high-entropy (HE) materials, the concept of HE has been extended to 2D MXenes by incorporating multiple transition metals, non-metallic elements, or both. The exceptional performance and properties of recently discovered HE MXenes suggest a new paradigm for designing advanced 2D materials by adjusting their chemical compositions. Although this vast compositional space offers many opportunities for HE MXenes, it also complicates the fundamental understandings and design principles of these 2D materials.

This proposed study aims to develop an advanced scientific computational framework by integrating high-throughput first-principles calculations, advanced machine learning techniques, and other data-driven methods to predict the stability and synthesizability of HE 2D MXenes across a broad compositional and structural space. This computational framework will accelerate the design process of HE MXenes and offer valuable insights for their experimental fabrication. The project will collaborate with the DOE Oak Ridge National Laboratory (ORNL), which will provide world-class computational facilities to support the rapid development of this framework, conduct high-throughput calculations, and train machine learning models. Finally, this project will create many research opportunities for training both graduate and undergraduate students, including those from underrepresented groups, at the University of Alabama.