Vacancy diffusion in transition metal diborides using diverse machine-learning potentials

Machine-learning interatomic potential (MLIP)-based molecular dynamics enables efficient exploration of mass-transport mechanisms in complex materials together with quantitative determination of diffusion coefficients at finite temperatures. Using an identical training dataset, we develop and assess three MLIP formalisms [moment tensor potentials (MTP), atomic cluster expansion (ACE), and message passing ACE (MACE)] and evaluate their transferability for diffusion simulations in hexagonal transition metal diborides. After benchmarking against 0 K ab initio migration energies and comparison of computational efficiency, the MTP model is employed to investigate boron-vacancy diffusion in α-TiB2, α-TaB2, and ω-WB2, representing group IV–VI transition metal diborides. Two distinct monovacancy diffusion pathways are identified within the puckered boron layer of ω-WB2 and analyzed as a function of temperature. The combined effects of strain and temperature on diffusivity are further examined, revealing a weak sensitivity to external stress under conditions relevant to high-temperature machining applications. Overall, nanosecond-scale finite-temperature simulations demonstrate the reliability and practical applicability of MLIPs for studying diffusion phenomena in complex ceramic materials.