Calculation update! New properties have been added to the website for dislocation monopole core structures, dynamic relaxes of both crystal and liquid phases, and melting temperatures! Currently, the results for these properties predominately focus on EAM-style potentials, but the results will be updated for other potentials as the associated calculations finish. Feel free to give us feedback on the new properties so we can improve their representations as needed.
Warning! Note that elemental potentials taken from alloy descriptions may not work well for the pure species. This is particularly true if the elements were fit for compounds instead of being optimized separately. As with all interatomic potentials, please check to make sure that the performance is adequate for your problem.
Citation: F. Brunner, D. Fioravanti, and F. Maresca (2026), "Liquid metal embrittlement in Fe-Zn: What can we learn from atomistic simulations?", Acta Materialia308, 121989. DOI: 10.1016/j.actamat.2026.121989.
Abstract: Liquid metal embrittlement (LME) is a bottleneck for applications such as welding of galvanised steels in the automotive industry. The mechanistic origin of this phenomenon is not fully understood. In this work, we address some of the key LME hypotheses by developing and thoroughly validating a machine learning interatomic potential for the Fe-Zn binary system. The interatomic potential is suited for reproducing with good accuracy relevant benchmark properties, dislocation- and crack-related characteristics, as well as intermetallic phases of the Fe-Zn binary system. Based on the validated potential, we conduct a series of numerical fracture toughness tests (K-tests) on (110)[-110] single crystals and Σ3, as well as Σ5 grain boundaries in Fe with varying Zn solute concentrations. The K-test results demonstrate the general applicability of the potential for studying complex fracture phenomena at the atomic scale. Furthermore, the conducted simulations suggest that for the prediction of LME, detailed analyses, including realistic Zn distributions, possible formation of intermetallic phases at the crack tip, and combination with microscale modelling, are required. We therefore discuss how machine learning interatomic potentials, like the one presented here, can be used in follow-up fracture studies to aid a more profound understanding of fracture and embrittlement phenomena in the Fe-Zn system.
See Computed Properties Notes: These files were provided by Sergei Starikov on July 15, 2026. The .asi file can be used with pace/extrapolation to perform additional active learning. The link was pulled from the paper and contains training and testing data. File(s):