Enhancing the hydrogen embrittlement (HE) resistance of alloys caters to the urgent needs of engineering safety and long-distance hydrogen transportation. Highly dense precipitates in the alloys act as H traps, however, some of them cannot strongly trap H thus failing to prevent its accumulation at the critical regions. Experimentally, it is challenging to expeditiously identify and generate phases causing strengthening and acting as strong H traps. Here, we demonstrate a computation-based design strategy to generate precipitates strongly trapping H. Based on the quantum machine learning Al-Sc-Cu potential, the optimal processing parameters of strong H trapping phases are determined, even though they are metastable in nature. Elemental mapping in electron microscope and atom probe tomography confirms the presence of Cu in Al₃Sc and its strong interaction with H. Hence, we envisage the proposed strategy will accelerate the design of HE-resistant microstructures of various technologically relevant materials via identification of desirable phases.

An attractive approach to mitigate hydrogen embrittlement (HE) is to use nano-sized particles to immobilize hydrogen. However, the atomic scale relationship between different particle-matrix characteristics in aluminum alloys and the susceptibility to HE is unknown. In this study, the effects of interactions between various interfaces and hydrogen in aluminum alloys are investigated using a comprehensive multiscale experimental and simulation-based approach that includes atomic-scale observations, simulation and advanced hydrogen mapping techniques. Depending on the nature of interfaces, e.g., coherency, size, and crystal structure, some are useful for mitigating HE, others provide hydrogen to sensitive sites, and some act as crack initiation sites.