UC Irvine

Dislocations, the line defects in a crystal lattice, dictate the material strength and deformation behaviors. The emergence of multi-principal element alloys (MPEAs) introduces a vast compositional space and local chemical fluctuations, inevitably impacting dislocation behaviors. There are vital fundamental questions remain to be understood: (i) how or whether dislocations behave differently in MPEAs than in traditional alloys; (ii) to what extent the local chemical variations influence the dislocation motion and the underlying energy landscape; and (iii) how to explore the dislocation behaviors in the ample compositional space.

This thesis utilizes atomistic simulation techniques, theoretical models, and machine learning methods to address these questions and reveal dislocation motions in MPEAs. By sampling and reconstructing the potential energy landscape of screw dislocation, it shows the landscape has a hierarchical and multilevel structure, which exerts a trapping force and back stress on dislocation motion, consequently retarding dislocation movement. With the introduced chemical short-range order (SRO), the energy barriers for migration mechanisms of screw dislocation increase but to different extents, leading to the dominant mechanism drifting from kink-glide to kink-pair nucleation. Concerning edge dislocation, the effect of SRO shows a cross-over from strengthening to softening, driven by the faster decrease of barriers in system with SRO under applied stress. This is related to the larger activation volume in the presence of SRO and diffuse anti-phase boundary generation. Lastly, a neural network model is proposed to predict Peierls barrier in the vast compositional space with significant local chemical fluctuation. By incorporating two inputs-local atomic type and atomic displacement field, the model captures the chemistry and structure of dislocation, enabling efficient prediction of its barriers. In conclusion, this thesis provides valuable insights into the behavior of dislocations in MPEAs, shedding light on the influence of local chemical fluctuation, and offers an efficient approach to exploring dislocation behaviors in diverse compositional spaces.