Modelling the dynamics of the martensitic transformation in Ni2MnGa with machine-learning force-fields
Abstract
The martensitic transformations in stoichiometric Ni2MnGa 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 Ni2MnGa, including the soft TA2 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.
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BibTeX
@misc{gruner2026modelling,
title = {{Modelling the dynamics of the martensitic transformation in Ni$_2$MnGa with machine-learning force-fields}},
author = {Markus E. Gruner and Yulia Klunnikova and Mike J. Bruckhoff and Konstatin P. Skokov and Olga N. Miroshkina},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.16467},
eprint = {2610.16467},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.16467},
note = {CC BY-SA 4.0},
}
RIS
TY - UNPB
TI - Modelling the dynamics of the martensitic transformation in Ni_2MnGa with machine-learning force-fields
AU - Markus E. Gruner
AU - Yulia Klunnikova
AU - Mike J. Bruckhoff
AU - Konstatin P. Skokov
AU - Olga N. Miroshkina
PY - 2026
DA - 2026/10/12
PB - arXiv
DO - 10.48550/arxiv.2610.16467
UR - https://arxiv.org/abs/2610.16467
AB - The martensitic transformations in stoichiometric Ni_2MnGa 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_2MnGa, 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.
ER -