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A nanocrystalline metal's strength increases significantly as its grain sizedecreases, a phenomenon known as the Hall-Petch relation. Such relation,however, breaks down when the grains become too small. Experimental studieshave circumvented this problem in a set of Ni-Mo alloys by stabilizing thegrain boundaries (GB). Here, using atomistic simulations with a machinelearning-based interatomic potential (ML-IAP), we demonstrate that the inverseHall-Petch relation can be correctly reproduced due to a change in the dominantdeformation mechanism as the grain becomes small in the Ni-Mo polycrystals. Itis found that the atomic von Mises strain can be significantly reduced byeither solute doping and/or annealing for small-grain-size polycrystals,leading to the increased strength of the polycrystals. On the other hand, forlarge-grain-size polycrystals, annealing weakens the material due to the largeatomic movements in GB regions. Over a broad range of grain size, thesuperposition of the solute and annealing effects on polycrystals enhances thestrength of those with small grain size more than those with large ones, givingrise to the continuous strengthening at extremely small grain sizes. Overall,this study not only demonstrates the reliability of the ML-IAP, but alsoprovides a comprehensive atomistic view of complex strengthening mechanisms innanocrystals, opening up a new avenue to tailor their mechanical properties.

Complex strengthening mechanisms in nanocrystalline Ni-Mo alloys revealed by a machine-learning interatomic potential