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

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

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https://tudatalib.ulb.tu-darmstadt.de/handle/tudatalib/4188
https://doi.org/10.48328/tudatalib-1394

DFG Classification

4.32-04 Computergestütztes Werkstoffdesign und Simulation von Werkstoffverhalten von atomistischer bis mikroskopischer Skala

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Except where otherwise noted, this license is described as CC BY 4.0 - Attribution 4.0 International

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

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