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

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Abstract
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.
Funding Information
  • Vetenskapsrådet (VR-2021-04426)
  • Vetenskapsrådet (VR-2025-04266)
  • VINNOVA (2022-03071)
  • Knut och Alice Wallenbergs Stiftelse (KAW2016.0358)
  • Knut och Alice Wallenbergs Stiftelse (KAW2019.0290)
  • Austrian Science Fund (10.55776/PAT4425523)
  • Österreichische Forschungsförderungsgesellschaft (Bridge)
  • Österreichische Forschungsförderungsgesellschaft (iDAP+)
  • Vetenskapsrådet (2022-06725)
  • EPSRC Centre for Doctoral Training in Modelling of Heterogeneous Systems (EP/S022848/1)

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