GASP

GASP (Genetic Algorithm for Structure Prediction) has been used to predict new compounds in several bulk and layered systems listed below. Click on any of the below datasets to receive the complete set of structural and energetic data. Interactive data visualization and machine learning tools on these datasets are coming soon!

Li-Ge Dataset paper

Dataset Citation
AlNi S. Honrao, S. R. Xie, and R. G. Hennig, Augmenting Machine Learning of Energy Landscapes with Local Structural Information, Journal of Applied Physics, submitted (2020).
CdTe S. Honrao, S. R. Xie, and R. G. Hennig, Augmenting Machine Learning of Energy Landscapes with Local Structural Information, Journal of Applied Physics, submitted (2020).
LiGe W. W. Tipton, C. A. Mathulis, and R. G. Hennig. Ab-initio prediction of the Li5Ge2 Zintl compound. Comp. Mater. Sci. 93, 133 (2014)

You can also find out more about GASP on our GASP GitHub page Original publications of these datasets are provided below