Temperature Effect on Phonon Dispersion Stability of Zirconium by Machine Learning-driven Atomistic Simulations
Xin Qian, Ronggui Yang

TL;DR
This paper develops a machine learning interatomic potential for Zirconium to accurately predict phonon dispersion and phase stability at high temperatures, overcoming limitations of traditional methods and empirical potentials.
Contribution
It introduces a novel machine learning-based interatomic potential for Zirconium that captures anharmonic effects and phase stability, enabling accurate finite-temperature phonon calculations.
Findings
Successfully modeled phonon dispersion at high temperature (1188 K).
Revealed the origin of BCC phase instability from the double-well potential energy surface.
Demonstrated stabilization of BCC phase due to atomic vibrations and dynamical averaging.
Abstract
It is well known that conventional harmonic lattice dynamics cannot be applied to energetically unstable crystals at 0 K, such as high temperature body centered cubic (BCC) phase of crystalline Zr. Predicting phonon spectra at finite temperature requires the calculation of force constants to the third, fourth and even higher orders, however, it remains challenging to determine to which order the Taylor expansion of the potential energy surface for different materials should be cut off. Molecular dynamics, on the other hand, intrinsically includes arbitrary orders of phonon anharmonicity, however, its accuracy is severely limited by the empirical potential field used. Using machine learning method, we developed an inter-atomic potential for Zirconium crystals for both the hexagonal closed-packed (HCP) phase and the body centered cubic phase. The developed potential field accurately…
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