Work

Crystal Structure Prediction and 0 K Stability Assessment of Hypothetical Re3Zr Polymorphs: Vibrational, Elastic, and Electronic Properties

Year: 2026

Type: article

Abstract: Re3Zr has recently been proposed in the cubic Pm-3n structure. Its alternative atomic arrangements and thermodynamic position within the Re–Zr system have not been systematically resolved. In this work, CALYPSO particle-swarm structure searches combined with density functional theory identify five Re3Zr polymorphs with Cmcm, Fmmm, Immm, P4/mmm, and Fm-3m symmetries and compare them consistently with the reported Pm-3n phase. Formation enthalpies, a selected-phase partial convex hull, phonon dispersions, elastic properties, sound velocities, elastic Debye temperatures, electronic structures, and bonding characteristics are evaluated. All five CALYPSO structures lie below Pm-3n in fixed-composition energy, with Cmcm being the lowest. The Cmcm phase has a formation enthalpy of −0.257 eV atom−1 and lies approximately 0.042 eV atom−1 above the selected-phase partial hull, identifying it as a low-lying off-hull metastable candidate at 0 K. AIMD simulations at 300 and 1000 K show no obvious structural reconstruction of the Cmcm framework over the simulated time scales. All six structures exhibit no imaginary phonon frequencies and satisfy the corresponding mechanical-stability criteria. Their elastic responses are strongly structure dependent: P4/mmm has the largest Chen–Tian hardness estimates and elastic Debye temperature, whereas Fm-3m shows the weakest elastic anisotropy. All six polymorphs are metallic, with Fermi-level states dominated by Re-5d orbitals and predominantly delocalized bonding. These results provide a thermodynamically constrained 0 K reference for future synthesis, phase identification, and finite-temperature or kinetic investigations of hypothetical Re3Zr polymorphs.

Source: Materials

DOI: 10.3390/ma19183844

Authors: Mardan Mamatyusup, Qun Wei, Zhengzhe Lin, Jing Luo, Meiguang Zhang

Institutions: Xidian University, Baoji University of Arts and Sciences

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,137

Funders: National Natural Science Foundation of China, Natural Science Basic Research Program of Shaanxi Province