GeTe that exhibits a strong anharmonicity and a ferroelectric phase transition between the rhombohedral and cubic structures has emerged as one of the leading thermoelectric materials.Herein,combining molecular dynami...GeTe that exhibits a strong anharmonicity and a ferroelectric phase transition between the rhombohedral and cubic structures has emerged as one of the leading thermoelectric materials.Herein,combining molecular dynamics simulations and inelastic neutron scattering measurements,the lattice dynamics in GeTe have been investigated to reveal the soft-mode mechanisms across the phase transition.We have constructed a first-principles-based machine-learning interatomic potential,which successfully captures the dynamical ferroelectric phase transition of GeTe by adopting the neural network technique.Although the low-energy acoustic phonons remain relatively unaffected at elevated temperatures,the high-energy optical,and longitudinal acoustic phonons demonstrate strong renormalizations as evidenced from the vibrational phonon spectra,which are attributed to the large anharmonicity accompanying the phase transition.Furthermore,our results reveal a nonmonotonic temperature dependence of the soft-modes beyond the perturbative regime.The insight provided by this work into the soft-modes may pave the way for further phonon engineering of GeTe and the related thermoelectrics.展开更多
基金This work is supported by the Zhejiang Provincial Natural Science Foundation(LR19A040001)the Research Grants Council of Hong Kong(17201019 and 17300018)+2 种基金the National Natural Science Foundation of China(11874313)the National Key Research and Development Program of China(2019YFA0209904)The authors are grateful for the research computing facilities offered by ITS,HKU.
文摘GeTe that exhibits a strong anharmonicity and a ferroelectric phase transition between the rhombohedral and cubic structures has emerged as one of the leading thermoelectric materials.Herein,combining molecular dynamics simulations and inelastic neutron scattering measurements,the lattice dynamics in GeTe have been investigated to reveal the soft-mode mechanisms across the phase transition.We have constructed a first-principles-based machine-learning interatomic potential,which successfully captures the dynamical ferroelectric phase transition of GeTe by adopting the neural network technique.Although the low-energy acoustic phonons remain relatively unaffected at elevated temperatures,the high-energy optical,and longitudinal acoustic phonons demonstrate strong renormalizations as evidenced from the vibrational phonon spectra,which are attributed to the large anharmonicity accompanying the phase transition.Furthermore,our results reveal a nonmonotonic temperature dependence of the soft-modes beyond the perturbative regime.The insight provided by this work into the soft-modes may pave the way for further phonon engineering of GeTe and the related thermoelectrics.
