Abstract: Martensitic transformation plays a pivotal role in strengthening and hardening of steels, yet an accurate interatomic potential for a comprehensive description of the martensitic phase formation in Fe-C alloys is lacking. Herein, we developed a deep learning-based interatomic potential to perform molecular dynamics (MD) simulations to study the martensitic phase transformation across a range of carbon (C) concentrations. The results revealed that an increased C concentration leads to a suppressed phase boundary movement and a decelerated phase transformation rate. To overcome the timescale limitations inherent in MD simulations, metadynamics sampling was employed to accelerate the simulations of C diffusion. We found that C atoms tend to cluster at distances equivalent to the lattice parameter of Fe with the same sublattice occupation, leading to local lattice tetragonality. Such C-ordered structures effectively inhibit dislocation movement and enhance strength. The stress field induced by dislocations facilitates a higher degree of ordering, and the formation of C-ordered structures was identified as a potentially crucial strengthening mechanism for martensitic steels. The consistency between our simulation results and reported experimental observations underscores the effectiveness of the developed DP model in simulating martensitic phase transformation in Fe-C alloys, providing detailed insights into the mechanisms underlying this process. This work not only advances the understanding of martensitic phase transformations in Fe-C alloys but also establishes a powerful computational framework for designing steels with optimized mechanical properties through the precise control of carbon ordering and dislocation behavior.

Key words: Martensite phase transformation, Molecular dynamics, Carbon ordering, Deep learning potential, Metadynamics sampling