Transferability of Zr-Zr interatomic potentials

A Set of Moment Tensor Potentials for Zirconium with Increasing Complexity

“…As shown in a study by Nicholls et al, current interatomic force fields fail to capture many of these key physical properties of Zr, which imposes important compromises on their users. Both embedded atom method (EAM) models and machine learning force fields (MLFFs) significantly underestimate zirconium’s melting point, with the exception of an EAM model specifically designed to model its melting point; the latter, which captures the correct melting point, drastically overestimates vacancy migration barriers.…”

“…Qian and Yang created Gaussian approximation potential , models for investigating the effect of temperature on phonon dispersions in Zr, but these models perform poorly in some out-of-sample scenarios, such as when considering Zr dimers in vacuum . Zong et al adopted a kernel ridge regression model to capture the martensitic phase transformations in Zr, but this resulted in a low melting point . Nitol et al modeled the transformations among α, β, and ω phases of Zr and Ti using artificial neural networks as regression models, and Liyanage et al developed an artificial neural network potential for hcp Zr that focused on extended defect properties.…”

“…MLFFs open the door to addressing these issues. − However, some recent attempts have not been fully successful in capturing all of the critical properties. Qian and Yang created Gaussian approximation potential , models for investigating the effect of temperature on phonon dispersions in Zr, but these models perform poorly in some out-of-sample scenarios, such as when considering Zr dimers in vacuum . Zong et al adopted a kernel ridge regression model to capture the martensitic phase transformations in Zr, but this resulted in a low melting point .…”