Dataset for "A machine learning approach to model solute grain boundary segregation"

DOI

Dataset for the publication "A machine learning approach to model solute grain boundary segregation", Huber, Hadian, Grabowski, and Neugebauer, npj Comp. Mat., doi:10.1038/s41524-018-0122-7 Data consists of atomic structure files and corresponding dilute segregation energies/site volumes/site coordinations/model energy predictions for grain boundaries in Al with Mg, Ti, Fe, Co, Ni, and Pb used as segregating elements. Please refer to the manuscript listed above for further details.

Identifier
DOI https://doi.org/10.17617/3.5TNVRM
Metadata Access https://edmond.mpg.de/api/datasets/export?exporter=dataverse_json&persistentId=doi:10.17617/3.5TNVRM
Provenance
Creator Huber, Liam
Publisher Edmond
Publication Year 2018
OpenAccess true
Contact huber(at)mpie.de
Representation
Language English
Resource Type Dataset
Version 1
Discipline Other