First-principles machine-learning quantum dynamics at 0K in SrTiO₃: light-induced ultrafast ferroelectric transition

Low temperature nonequilibrium quantum dynamics in crystals is extremely challenging, and has not been possible to perform on materials of realistic complexity. In this work we develop a novel technique, the time-dependent self-consistent harmonic approximation (TDSCHA [1]), and use it to simulate the quantum dynamics in SrTiO₃ at 0K. We combine TDSCHA with state-of-the-art machine learning force fields to accelerate calculations by multiple orders of magnitude compared to first principles dynamics.

We study the light-induced ferroelectric transition in SrTiO₃, where nuclear quantum fluctuations play a major role in stabilizing the paraelectric phase at low temperatures.

Our approach allows for an unprecedented description of the light-induced ferroelectric transition due to the absence of empirical parameters, paving the way to the development of next-generation ultrafast nonvolatile memories.

[1] Lorenzo Monacelli and Francesco Mauri, Time-dependent self-consistent harmonic approximation: Anharmonic nuclear quantum dynamics and time correlation functions, Phys. Rev. B 103104305 (2021)