Research data for "Crystal structure identification with 3D convolutional neural networks with application to high-pressure phase transitions in SiO2"
Description
This dataset supports the paper "Crystal structure identification with 3D convolutional neural networks with
application to high-pressure phase transitions in SiO2".
The following files are provided:
-The training database for the simple (artificial and MD) and the SiO2 structures
--> The training data is provided in two different formats. In the "simple_training_dump" and "SiO2_training_dump" files, the dump files from the MD trajectories are provided. In the "simple_training_extracted" and "SiO2_training_extracted" files 1,000,000 extracted atomic environments in a numpy format are stored.
-The holdout dataset for the simple structures
-The snapshots of the SiO2 shock simulation
DFG subject classification
4.32-04 Computergestütztes Werkstoffdesign und Simulation von Werkstoffverhalten von atomistischer bis mikroskopischer SkalaURI
https://tudatalib.ulb.tu-darmstadt.de/handle/tudatalib/4188https://doi.org/10.48328/tudatalib-1394
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Except where otherwise noted, this item's license is described as Creative Commons Attribution 4.0