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Artificial neural network potentials (NNPs) have emerged as effective toolsfor understanding atomic interactions at the atomic scale in various phenomena.Recently, we developed highly transferable NNPs for {\alpha}-iron and{\alpha}-iron/hydrogen binary systems (Physical Review Materials 5 (11),113606, 2021). These potentials allowed us to investigate deformation andfracture in {\alpha}-iron under the influence of hydrogen. However, thecomputational cost of the NNP remains relatively high compared to empiricalpotentials, limiting their applicability in addressing practical issues relatedto hydrogen embrittlement. In this work, building upon our prior research oniron-hydrogen NNP, we developed a new NNP that not only maintains the excellenttransferability but also significantly improves computational efficiency (morethan 40 times faster). We applied this new NNP to study the impact of hydrogenon the cracking of iron and the deformation of polycrystalline iron. Weemployed large-scale through-thickness {110}<110> crack models and large-scalepolycrystalline {\alpha}-iron models. The results clearly show that hydrogenatoms segregated at crack tips promote brittle-cleavage failure followed bycrack growth. Additionally, hydrogen atoms at grain boundaries facilitate thenucleation of intergranular nanovoids and subsequent intergranular fracture. Weanticipate that this high-efficiency NNP will serve as a valuable tool forgaining atomic-scale insights into hydrogen embrittlement.

Highly efficient and transferable interatomic potentials for {\alpha}-iron and {\alpha}-iron/hydrogen binary systems using deep neural networks