Published in

American Geophysical Union, Journal of Geophysical Research. Solid Earth, 3(129), 2024

DOI: 10.1029/2024jb028656

Links

Tools

Export citation

Search in Google Scholar

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

Journal article published in 2024 by Xuyan Cao ORCID, Songsong Han, Junwei Li, Sheng‐Cai Zhu ORCID, Qingyang Hu ORCID
This paper was not found in any repository, but could be made available legally by the author.
This paper was not found in any repository, but could be made available legally by the author.

Full text: Unavailable

Green circle
Preprint: archiving allowed
Green circle
Postprint: archiving allowed
Orange circle
Published version: archiving restricted
Data provided by SHERPA/RoMEO

Abstract

AbstractRocks 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.