Exploring the energy landscape of aluminas through machine learning interatomic potential

Despite the widespread applications of alumina due to its rich polymorphism, the structures of many transitional aluminas remain unresolved. This work employs the neuroevolution potential (NEP) approach to accurately describe polymorphic aluminas. Its accuracy and generality are validated through molecular dynamics simulations under diverse thermodynamic and structural conditions. A structural search workflow has also been developed based on NEP, which, in conjunction with spectroscopic data and structural stability considerations, supports the energetic preference of the Smr\ifmmode \check{c}\else \v{c}\fi{}ok model over the Luo model for $\ensuremath{\gamma}$-Al${}_{2}$O${}_{3}$. This methodological framework provides a systematic approach for exploring polymorphic materials with intrinsic defects, such as Ga${}_{2}$O${}_{3}$.