A multi-valued machine-learned dipole model with oxidation-number corrections enables electric-field-driven molecular dynamics for liquids and solids, demonstrated on water and LiNbO3.
Ultrafast quantum dynamics in $\mathbf{\mathrm{SrTiO_3}}$ under impulsive THz radiation
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abstract
Ultrafast spectroscopy paved the way for probing transient states of matter produced through photoexcitation. Despite significant advances, the microscopic processes governing the formation of these states remain largely unknown. This study discloses the nuclear quantum dynamics of $\mathrm{SrTiO_3}$ when excited by THz laser pumping. We use a first-principles machine-learning approach accounting for all atomistic degrees of freedom to examine the time-resolved energy flow across phonon modes following the photoexcitation, revealing the mechanism underpinning the observed phonon upconversion and quantifying the lifetime of the out-of-equilibrium motion. Crucially, our simulations predict that THz pump pulses can generate persistent out-of-equilibrium stress capable of inducing polar order. We observe a correlation between the experimentally measured lifetime of the transient inversion-symmetry-broken state and the duration of the out-of-equilibrium nuclear state. This work not only explains the experimental results on $\mathrm{SrTiO_3}$ but also establishes a framework for simulating the photoexcited quantum dynamics of nuclei from first principles without any empirical input. It lays the groundwork for systematic explorations of complex materials sensitive to photoexcitation.
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cond-mat.mtrl-sci 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
unclear 1representative citing papers
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Electric-Field Driven Nuclear Dynamics of Liquids and Solids from a Multi-Valued Machine-Learned Dipolar Model
A multi-valued machine-learned dipole model with oxidation-number corrections enables electric-field-driven molecular dynamics for liquids and solids, demonstrated on water and LiNbO3.