Controlling MXene termination ensembles for stability and performance

Abstract The promise of MXene surface chemistry lies in tuning T $$_{x}$$ x , yet the relevant design variable is not the identity of an individual surface terminating group but the ensemble of local surface environments. In M $$_{n+1}$$ n + 1 X $$_{n}$$ n T $$_{x}$$ x MXenes, where M is an early transition metal and X is C or N, T $$_{x}$$ x is treated here as a functional termination ensemble: the collection of local termination environments that exist across MXene surfaces. These environments are defined by chemical composition, binding-site geometry, coverage, spatial distribution, coordination, defects, intercalants, and edge structures. Because these ensembles evolve during synthesis, storage, processing, and operation, they determine whether MXenes remain structurally intact and functionally useful within application-specific performance–stability windows. We argue that predictive design requires computational approaches that move beyond idealized single-termination models by combining density functional theory, molecular dynamics, statistical sampling, surrogate models, and actively learned machine learning interatomic potentials to map termination ensembles under operating conditions. Coupled with synthesis–characterization feedback, this performance–stability framework links application-defined functions and operating windows to ensembles that can be installed, retained, or deliberately transformed while co-optimizing performance and stability.