Abstract
In refractory multi-principal element alloys (RMPEAs), the rapid atomic diffusion occurring near dislocations facilitates local segregation and chemical ordering, leading to the formation of unique atomic environments capable of pinning dislocations on slip planes. However, previous atomistic simulations have largely overlooked how dislocations induce these unique atomic environments and influence the strengthening mechanism. In this study, we systematically investigate the atomic environments generated by dislocations during annealing and their effects on the mechanical properties of body-centered-cubic (BCC) RMPEAs using hybrid Monte Carlo/molecular dynamics simulations. A machine-learning interatomic potential is specifically trained for these RMPEAs. Our results reveal that the dislocation-core energy, elemental mixing energy, and dislocation-stress field collectively determine unique atomic environments, which strongly pin dislocations and significantly increase the critical resolved shear stress. As the atomic rearrangement near the dislocation core progresses, the enhanced pinning effect of edge dislocations arises from the continuous narrowing of the dislocation-core width, while the increased pinning of screw dislocations is attributed to the dislocation line becoming more kinked. In particular, edge dislocations exhibit a much stronger pinning effect than screw dislocations, consistent with recent experimental results.
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Data availability
All data generated, used, and/or analyzed during the current study are available on request from Xiang-Guo Li (lixguo@mail.sysu.edu.cn). The MTP potential and its training data have been published in an open repository (https://github.com/ucsdlxg/CrMoNbTaVW-ML-interatomic-model).
Code availability
The DFT calculations were performed with the Vienna ab initio simulation package. The training of MTP potential used the MAML (Materials machine learning) package. The LAMMPS package was used to perform MD/MC simulations. All the other codes that support the findings of this study are available from Xiang-Guo Li (lixguo@mail.sysu.edu.cn) upon reasonable request.
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Acknowledgements
Xiang-Guo Li would like to acknowledge financial support from the Guangdong Basic and Applied Basic Research Foundation (2025A1515011961), the Fundamental Research Funds for the Central University, Sun Yat-Sen University (24qnpy322), and the Shenzhen Science and Technology Program (Grant No. JCYJ20241202130018024). Xiang-Guo Li, Yuhao Luo, Tianyi Wang and Zhihao Huang also acknowledge the use of computing resources from the Tianhe-2 Supercomputer.
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Yuhao Luo: Writing—original draft, Visualization, Formal analysis, Methodology, Investigation, Data curation. Tianyi Wang: Writing— original draft, Visualization, Formal analysis, Methodology, Investigation, Data curation. Zhihao Huang: Visualization, Formal analysis, Methodology, Investigation, Data curation. Yanqing Su: Writing—review & editing, Investigation, Methodology. Shuozhi Xu: Writing— review & editing, Validation, Conceptualization. Peter K. Liaw: Writing—review & editing, Supervision, Resources. Xiang-Guo Li: Writing—review & editing, Funding acquisition, Supervision, Resources, Conceptualization.
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Xiang-Guo Li is a guest editor of the npj Computational Materials special collection “Machine Learning Interatomic Potentials in Computational Materials”. He was not involved in the journal’s review of or decisions related to this manuscript. The other authors declare that they have no financial or non‑financial competing interests.
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Luo, Y., Wang, T., Huang, Z. et al. Dislocation-induced ordering as a source of strengthening in refractory multi-principal element alloys. npj Comput Mater (2026). https://doi.org/10.1038/s41524-026-02008-x
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DOI: https://doi.org/10.1038/s41524-026-02008-x
