Data underlying the research of Kinetic and thermodynamic transition pathways of silica by machine learning: implication for meteorite impacts

doi: 10.4121/c881f6f4-3217-439e-8331-026bce99e9f7.v1
The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/c881f6f4-3217-439e-8331-026bce99e9f7
Datacite citation style:
Cao, Xuyan (2024): Data underlying the research of Kinetic and thermodynamic transition pathways of silica by machine learning: implication for meteorite impacts. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/c881f6f4-3217-439e-8331-026bce99e9f7.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite
Dataset

We construct the potential energy surface of silica under various pressure conditions using machine learning potential and have refined three unique pressure windows, either kinetically or thermodynamically favored, to stabilize seifertite, which reached an agreement with observations in meteorites.

history
  • 2024-01-10 first online, published, posted
publisher
4TU.ResearchData
format
gzipped shape files
organizations
Center for High Pressure Science & Technology Advanced Research, Collaborative Research for Earth and Applied Materials (CREAM), Beijing, China

DATA

files (1)