Application of the GIPAW method to calculate the nuclear quadrupole moment of 57Fe

A. V. GIL REBAZA; C. G. BRUSASCO; A. M. MUDARRA; E. PELTZER Y BLANCÁ; S. COTENIER; L. A. ERRICO

Brasov

Conferencia; International Conference on the Applications of the Mössbauer Effect, ICAME 2021 and 3rd International Conference on Hyperfine Interactions and their applications; 2021

National Institute of Materials Physics (NIMP)

One of the hyperfine parameters that can be determined in Mössbauer spectroscopy (MS) is the quadrupole splitting (QS) that provide information of the asymmetry of the change distribution in the subnanoscopic neighborhood of a probe atom and it is proportional to the product of the principal component (Vzz) of the electric field gradient (EFG) and the nuclear quadrupole moment (QN).Even though QN is a nuclear quantity, are not well know the exact value for many isotopes. A method to determinate QN value is comparing calculations of the EFG based on the Density Functional Theory (DFT) with experimental values of QS. For these studies, the Full-Potential Linearized Plane-Wave (FP-LAPW) method has been the benchmark and in consequence was largely employed for the prediction of hyperfine parameters in general and the EFG in particular. More recently, the Gauge Including Projected Augmented Wave (GIPAW) formalism based on the Pseudopotential and PlaneWave as an all-electron method to determinate the EFG tensor. The advantage of the GIPAW calculations compared with FP-LAPW one is the computational resource required, specially for the case of large system. But teh accuracy and precision of this method must be determinate.In the present work, we have used the GIPAW method to determinate the EFG tensor, Vzz and the asymmetry parameter (η) for several Fe-based solid compounds. We compare it with the values predicted by the FP-LAPW method, and with the experimental data reported in the literature. We analyze the differences between the two set of computational predictions, and their impact on the accuracy of the QN prediction.