Data for training a machine learning interatomic potential that can be used to simulate a range of germanium–antimony–tellurium compositions—typical phase-change materials—under realistic device conditions.
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Staged for ingest following next database update.
Suggest naming datasets (there are 3) variations on "GST_GAP_22" or similar, as this is the name of the trained model given in the paper
Name
Josh Vita
Email
vita1@llnl.gov
Dataset name
phase_change_memory_materials_2023
Authors
Yuxing Zhou, Wei Zhang, Evan Ma, Volker L. Deringer
Links
Dataset description
Data for training a machine learning interatomic potential that can be used to simulate a range of germanium–antimony–tellurium compositions—typical phase-change materials—under realistic device conditions.
File details
No response
Method
No response
Method (other)
No response
Software
None
Software (other)
No response
Software version(s)
No response
Additional details
No response
Property types
No response
Other/additional property
No response
Property details
No response
Elements
No response
Number of Configurations
No response
Naming convention
No response
Configuration sets
No response
Configuration labels
No response
Distribution license
No response
Permissions