A particular challenge for new materials is that the atomic positions are generally not known. We present a machine learning model for the prediction of DFT-calculated formation energies based on Voronoi quotient graphs and local symmetry classification without the need for detailed information about atomic positions.
Jørgensen, Peter Bjørn, et al. "Materials property prediction using symmetry-labeled graphs as atomic position independent descriptors." Physical Review B 100.10 (2019): 104114.
Voronoi quotient graphs, local symmetry classification,
Curator: qinyang