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Kinetic pathways of coesite densification from metadynamics.

David Vrba1, Roman Martoňák1

  • 1Department of Experimental Physics, Faculty of Mathematics, Physics and Informatics, Comenius University, Mlynská Dolina F2, 84248 Bratislava, Slovakia.

The Journal of Chemical Physics
|September 19, 2025
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Summary

High pressure transforms coesite into new crystalline phases like coesite-IV and octahedral structures. Computational methods using machine learning potentials successfully modeled these complex structural transformations and their atomistic mechanisms.

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Area of Science:

  • Mineral Physics
  • Computational Materials Science
  • Geochemistry

Background:

  • Coesite, a high-pressure polymorph of SiO2, undergoes significant structural changes under extreme pressures.
  • Previous experimental studies identified metastable coesite-II and coesite-III, followed by reconstructive transformations into octahedral phases, coesite-IV, and coesite-V above 30 GPa.
  • Simulating these complex reconstructive transformations computationally presents a significant challenge due to the large structural rearrangements and coordination changes.

Purpose of the Study:

  • To computationally investigate the high-pressure compression of coesite and elucidate the atomistic mechanisms of its structural transformations.
  • To validate a computational approach combining metadynamics, machine learning potentials, and collective variables for studying complex phase transitions.
  • To identify and describe the distinct transformation pathways leading to observed experimental outcomes.

Main Methods:

  • Utilized metadynamics simulations with Si-O coordination number and volume as collective variables.
  • Employed an advanced machine-learning based atomistic potential (ACE potential) for accurate energy and force calculations.
  • Analyzed the atomistic mechanisms of transformation pathways, including amorphization, formation of coesite-IV, and octahedral phases.

Main Results:

  • The computational approach successfully reproduced all experimentally observed transformation pathways: amorphization, coesite-IV, and octahedral phases.
  • Detailed atomistic mechanisms for the transformation to coesite-IV and the two-step process for octahedral phases were described.
  • The pathway to coesite-IV was predicted to be favored at room temperature, while octahedral phase formation is more likely at 600 K.

Conclusions:

  • Machine learning potentials combined with metadynamics provide a powerful tool for studying complex reconstructive phase transitions in minerals.
  • The study clarifies the atomistic details of coesite compression, offering insights into Earth's deep interior and materials under extreme conditions.
  • Temperature-dependent pathway selection was predicted, highlighting the influence of thermal energy on high-pressure mineral transformations.