TUdatalib : Research data for "Crystal structure identification with 3D convolutional neural networks with application to high-pressure phase transitions in SiO2"

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dc.contributor.author	Erhard, Linus C.
dc.contributor.author	Utt, Daniel
dc.contributor.author	Klomp, Arne J.
dc.contributor.author	Albe, Karsten
dc.date.accessioned	2024-03-22T12:03:32Z
dc.date.available	2024-03-22T12:03:32Z
dc.date.issued	2024
dc.identifier.uri	https://tudatalib.ulb.tu-darmstadt.de/handle/tudatalib/4188
dc.identifier.uri	https://doi.org/10.48328/tudatalib-1394
dc.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	de_DE
dc.language.iso	en	de_DE
dc.rights	Creative Commons Attribution 4.0
dc.rights.uri	https://creativecommons.org/licenses/by/4.0/
dc.subject.classification	4.32-04 Computergestütztes Werkstoffdesign und Simulation von Werkstoffverhalten von atomistischer bis mikroskopischer Skala	de_DE
dc.subject.ddc	620
dc.title	Research data for "Crystal structure identification with 3D convolutional neural networks with application to high-pressure phase transitions in SiO2"	de_DE
dc.type	Dataset	de_DE
tud.unit	TUDa
tud.history.classification	Version=2020-2024;406-04 Computergestütztes Werkstoffdesign und Simulation von Werkstoffverhalten von atomistischer bis mikroskopischer Skala