Temperature-dependent anharmonic phonons in quantum paraelectric KTaO$_3$ by first principles and machine-learned force fields
Luigi Ranalli (1), Carla Verdi (1), Lorenzo Monacelli (2), Matteo, Calandra (3), Georg Kresse (1), Cesare Franchini (1, 4) ((1) University of, Vienna, Faculty of Physics, Center for Computational Materials Science,, (2) University of Rome, Sapienza, Department of Physics

TL;DR
This study combines first-principles calculations with machine learning to model temperature-dependent anharmonic phonons in quantum paraelectric KTaO$_3$, revealing the importance of anharmonic effects in stabilizing ferroelectric modes.
Contribution
It introduces a robust computational workflow integrating DFT and machine-learned force fields to efficiently simulate quantum paraelectric behavior over broad temperature ranges.
Findings
Anharmonic terms are crucial for stabilizing ferroelectric phonons.
Machine-learned force fields enable large-scale, efficient sampling.
The workflow accurately reproduces experimental observations.
Abstract
Understanding collective phenomena in quantum materials from first principles is a promising route toward engineering materials properties on demand and designing new functionalities. This work examines the quantum paraelectric state, an elusive state of matter characterized by the smooth saturation of the ferroelectric instability at low temperature due to quantum fluctuations associated with anharmonic phonon effects. The temperature-dependent evolution of the soft ferroelectric phonon mode in the quantum paraelectric KTaO in the range 0-300 K is modelled by combining density functional theory (DFT) calculations with the stochastic self-consistent harmonic approximation assisted by an on-the-fly machine-learned force field. The calculated data show that including anharmonic terms is essential to stabilize the spurious imaginary ferroelectric phonon predicted by DFT, in agreement…
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Taxonomy
TopicsMachine Learning in Materials Science
