Ultrafast Nonthermal Lattice Destabilization and Suppression of Polar Optical Scattering in Electronically Excited $α$-SiO$_2$ from First-Principles and Deep Neural Network Potential Modeling
We present a multiscale first-principles-to-machine-learning approach to investigate ultrafast lattice dynamics in electronically excited $α$-SiO$_2$. Ab initio molecular dynamics (AIMD) based on electronic-temperature-dependent density functional theory (DFT) are used to train electronic-temperature-dependent deep neural network potentials (DNNPs). The use of DNNPs enables atomistic modeling at near-DFT accuracy of large $α$-SiO$_2$ cells with thousands of atoms. In particular, DNNPs allowed us to obtain accurate phonon band structures and molecular dynamics (MD) of $α$-SiO$_2$ excited by a sudden increase in electronic temperature. With increasing electronic temperature, $T_e$, pronounced lattice destabilization of $α$-SiO$_2$ is found, as evidenced by violations of elastic stability criteria, substantial volumetric expansion, a sharp reduction of the bulk modulus, and progressive weakening of Si-O bonding due to antibonding-state occupation. From the electronic and phonon band structures, we estimated the Frohlich coupling constant, which decreases as $T_e$ increases, suggesting a crossover to a nonpolar phase of $α$-SiO$_2$ at elevated electronic temperature. This is corroborated by the Bader charge analysis. We also suggest that polar optical phonon scattering should be strongly suppressed at $T_e > 2$ eV. From large-cell DNNP-MD simulations, we show that a well-defined thermal equilibrium, as defined by the Maxwell-Boltzmann distribution, is not achieved over the first few hundred femtoseconds. This behavior explains the non-monotonic equilibration of the kinetic temperature after a sudden rise of $T_e$. After $T_e$ is raised to 2.6 eV, Si and O atoms first equilibrate separately at two different temperatures, suggesting an atomic fluid phase, in agreement with recent experimental and theoretical findings.