Understanding phase transitions of α-quartz under dynamic compression conditions by machine-learning driven atomistic simulations
Abstract Characteristic shock effects in quartz serve as a key indicator of historic impacts at geologic sites. Despite this geologic significance, atomistic details of structural transformations of quartz under high pressure and shock compression remain poorly understood. This ambiguity is evidence...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-03-01
|
| Series: | npj Computational Materials |
| Online Access: | https://doi.org/10.1038/s41524-025-01542-4 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850137927351271424 |
|---|---|
| author | Linus C. Erhard Christoph Otzen Jochen Rohrer Clemens Prescher Karsten Albe |
| author_facet | Linus C. Erhard Christoph Otzen Jochen Rohrer Clemens Prescher Karsten Albe |
| author_sort | Linus C. Erhard |
| collection | DOAJ |
| description | Abstract Characteristic shock effects in quartz serve as a key indicator of historic impacts at geologic sites. Despite this geologic significance, atomistic details of structural transformations of quartz under high pressure and shock compression remain poorly understood. This ambiguity is evidenced by conflicting experimental observations of both amorphization and transitions to crystalline polymorphs. Utilizing a newly developed machine-learning interatomic potential, we examine the response of α-quartz to shock compression with a peak pressure of 56 GPa over nanosecond timescales. We observe initial amorphization of quartz before crystallization into a d-NiAs-structured silica phase with disorder on the silicon sublattice, accompanied by the formation of domains with partial order of silicon. Investigating a variety of strain conditions of quartz enables us to identify non-hydrostatic stress and strain states that allow for direct diffusionless transformation to rosiaite-structured silica. |
| format | Article |
| id | doaj-art-b1b3d12f8f5c4a8a8f1d9e8c35ab0c96 |
| institution | OA Journals |
| issn | 2057-3960 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Computational Materials |
| spelling | doaj-art-b1b3d12f8f5c4a8a8f1d9e8c35ab0c962025-08-20T02:30:42ZengNature Portfolionpj Computational Materials2057-39602025-03-011111910.1038/s41524-025-01542-4Understanding phase transitions of α-quartz under dynamic compression conditions by machine-learning driven atomistic simulationsLinus C. Erhard0Christoph Otzen1Jochen Rohrer2Clemens Prescher3Karsten Albe4Institute of Materials Science, Technische Universität DarmstadtInstitute of Earth and Environmental Sciences, University of FreiburgInstitute of Materials Science, Technische Universität DarmstadtInstitute of Earth and Environmental Sciences, University of FreiburgInstitute of Materials Science, Technische Universität DarmstadtAbstract Characteristic shock effects in quartz serve as a key indicator of historic impacts at geologic sites. Despite this geologic significance, atomistic details of structural transformations of quartz under high pressure and shock compression remain poorly understood. This ambiguity is evidenced by conflicting experimental observations of both amorphization and transitions to crystalline polymorphs. Utilizing a newly developed machine-learning interatomic potential, we examine the response of α-quartz to shock compression with a peak pressure of 56 GPa over nanosecond timescales. We observe initial amorphization of quartz before crystallization into a d-NiAs-structured silica phase with disorder on the silicon sublattice, accompanied by the formation of domains with partial order of silicon. Investigating a variety of strain conditions of quartz enables us to identify non-hydrostatic stress and strain states that allow for direct diffusionless transformation to rosiaite-structured silica.https://doi.org/10.1038/s41524-025-01542-4 |
| spellingShingle | Linus C. Erhard Christoph Otzen Jochen Rohrer Clemens Prescher Karsten Albe Understanding phase transitions of α-quartz under dynamic compression conditions by machine-learning driven atomistic simulations npj Computational Materials |
| title | Understanding phase transitions of α-quartz under dynamic compression conditions by machine-learning driven atomistic simulations |
| title_full | Understanding phase transitions of α-quartz under dynamic compression conditions by machine-learning driven atomistic simulations |
| title_fullStr | Understanding phase transitions of α-quartz under dynamic compression conditions by machine-learning driven atomistic simulations |
| title_full_unstemmed | Understanding phase transitions of α-quartz under dynamic compression conditions by machine-learning driven atomistic simulations |
| title_short | Understanding phase transitions of α-quartz under dynamic compression conditions by machine-learning driven atomistic simulations |
| title_sort | understanding phase transitions of α quartz under dynamic compression conditions by machine learning driven atomistic simulations |
| url | https://doi.org/10.1038/s41524-025-01542-4 |
| work_keys_str_mv | AT linuscerhard understandingphasetransitionsofaquartzunderdynamiccompressionconditionsbymachinelearningdrivenatomisticsimulations AT christophotzen understandingphasetransitionsofaquartzunderdynamiccompressionconditionsbymachinelearningdrivenatomisticsimulations AT jochenrohrer understandingphasetransitionsofaquartzunderdynamiccompressionconditionsbymachinelearningdrivenatomisticsimulations AT clemensprescher understandingphasetransitionsofaquartzunderdynamiccompressionconditionsbymachinelearningdrivenatomisticsimulations AT karstenalbe understandingphasetransitionsofaquartzunderdynamiccompressionconditionsbymachinelearningdrivenatomisticsimulations |