Unveiling the mechanism of the carbon ordering and martensite tetragonality in Fe–C alloys studied via deep-potential molecular dynamics simulations

Abstract

The martensitic transformation plays a pivotal role in the 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 develop 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 reveal that an increased C concentration leads to a suppression of phase boundary movement and a deceleration of the phase transformation rate. To overcome the timescale limitations inherent in MD simulations, metadynamics sampling was employed to accelerate the simulations of C diffusion. We find 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. The formation of C-ordered structures is 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.