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

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.