Kinetic and Thermodynamic Transition Pathways of Silica by Machine Learning: Implication for Meteorite Impacts.

Rocks falling to Earth from space may generate pressure and temperature approaching Earth's deep mantle, but such meteorite impact only persists for a very short period. Under these extreme conditions, kinetical factors largely control mineral phase transitions, in which the resultant phase may deviate from those at thermal equilibrium. Here, we focus on the phase transitions of silica during meteorite impact, and have elucidated multiple pathways from low‐coordinated silica to seifertite, the densest known silica found in meteorite samples. Utilizing a high‐dimensional neuro‐network potential specifically designed for silica, we exhaustively map the potential energy landscape through stochastic surface walking and uncover low‐barrier transition pathways toward seifertite at pressures far away from thermal equilibrium. These kinetic‐driven transitions are then characterized by first‐principles simulations, revealing narrow transition windows of pressure, with seifertite becoming more kinetically favored over stishovite at pressures in the vicinity of 10 and 25 GPa. Our results suggest that meteorite impacts should have reached such target pressures to overcome the thermodynamic limit of forming seifertite. The presence of seifertite may provide key information in constraining the relevant dynamic compression conditions. Plain Language Summary: Meteorites, remnants of planetary parent bodies that have survived their fiery descent through Earth's atmosphere, offer a wealth of geophysical insights into the original meteoroid's velocity and density, the composition of its celestial body, and even the history of our solar system. However, due to the fleeting nature of meteorite impact and the extremely high pressure‐temperature conditions they experience, the phase transition of minerals within these meteorites may deviate from those observed in Earth's interiors. Using machine learning, our work reconstructed the energy landscapes of silica at various pressures, revealing transition pathways between cristobalite and seifertite even far below the thermal stability regime. These transition paths occur within very narrow pressure ranges, thus requiring reaching target pressure within a remarkably short timeframe, mirroring meteoritic impact. By studying transition mechanism of silica, people can reasonably calibrate the pressure and temperature conditions of meteoric impact from its chemical compositions, and decipher the details of meteoritic events. Key Points: The energy landscape of silica were explored using a machine learning potential at various pressures, reproducing experimental resultsMultiple kinetically favored transition paths have been identified far below the thermodynamic stability fieldsThe characteristics of these kinetically favored paths may pinpoint dynamic compression conditions experienced during meteorite impacts [ABSTRACT FROM AUTHOR]