Modelling the dynamics of the martensitic transformation in Ni${}_2$MnGa with machine-learning force-fields

The martensitic transformations in stoichiometric Ni${}_2$MnGa and related Heusler alloys are governed by a delicate interplay between electronic structure, magnetism, and lattice dynamics. While density functional theory (DFT) has successfully mapped the energy landscape of modulated martensites, it remains computationally prohibitive for capturing finite-temperature dynamics across relevant length and time scales. Here, we bridge this gap by constructing DFT-trained machine-learning force-fields (ML-FF) interpolated from ab initio molecular dynamics. We demonstrate that ML-FF trained on DFT data can reproduce key features of Ni${}_2$MnGa, including the soft TA${}_2$ phonon and a preference for nanotwinned martensites. Using classical molecular dynamics simulations, we explore the impact of adaptive nanotwinning on the austenite-martensite transition, which facilitates the dynamics of both, forward and backward transformation. We demonstrate, that the negative twin boundary energy along the entire transformations path predicted by DFT for $T=0$, supports the formation of ordered modulated martensites at finite temperatures. Our results pave the way for large-scale simulations of magnetic shape memory alloys with near-DFT accuracy.