Understanding phase transitions of $\alpha$-quartz under dynamic compression conditions by machine-learning driven atomistic simulations

TL;DR

This study uses machine-learning atomistic simulations to explore the complex phase transitions of $ ext{α}$-quartz under shock compression, revealing amorphization and recrystallization pathways relevant to geological impacts.

Contribution

It introduces a novel machine-learning interatomic potential to simulate high-pressure shock effects on $ ext{α}$-quartz, clarifying structural transformation mechanisms.

Findings

Amorphization occurs before recrystallization into d-NiAs-structured silica.

Partial silicon order domains form during recrystallization.

Specific non-hydrostatic stress states enable direct formation of rosiaite-structured silica.

Abstract

Characteristic shock effects in silica serve as a key indicator of historical impacts at geological sites. Despite this geological significance, atomistic details of structural transformations under high pressure and shock compression remain poorly understood. This ambiguity is evidenced by conflicting experimental observations of both amorphization and crystallization transitions. Utilizing a newly developed machine-learning interatomic potential, we examine the response of $\alpha$-quartz to shock compression with a peak pressure of 60 GPa over nano-second timescales. We initially observe amorphization before recrystallization into a d-NiAs-structured silica with disorder on the silicon sublattice, accompanied by the formation of domains with partial order of silicon. Investigating a variety of strain conditions enables us to identify the non-hydrostatic stress and strain states that allow the direct diffusionless formation of rosiaite-structured silica.