| Abstract Scope |
We introduce a robust computational framework that integrates first-principles density functional theory (DFT) with Monte Carlo (MC) simulations to uncover the minimum energy configurations (MECs) of high-entropy (HE) MXenes. This approach reveals a strong tendency for interlayer chemical segregation, leading to the formation of out-of-plane ordered MXenes (o-MXenes). For example, in \left({Ti}_{0.5}{Cr}_{0.5}\right)_4C_3, Cr atoms preferentially occupy the outermost layers. Similar segregation-driven ordering is observed in \left({Nb}_{0.5}{Mo}_{0.5}\right)_4C_3, \left({Cr}_{0.5}{Mo}_{0.5}\right)_4C_3, and \left({Ti}_{0.33}{Cr}_{0.33}{Mo}_{0.34}\right)_4C_3, aligning well with prior theoretical and experimental studies. To further confirm this behavior, we employ machine learning interatomic potentials (ML-IAPs) in classical molecular dynamics/MC simulations, which validate the DFT-predicted out-of-plane MXenes (o-MXenes). The proposed framework is not only accurate and predictive but also broadly transferable to other compositionally complex systems. It offers a powerful tool for exploring phase stability and chemical ordering in emerging two-dimensional materials, advancing the design of next-generation multicomponent materials. |