Crystal structure prediction in a continuous representative space

Abstract Here we report a method of finding multiple crystal structures similar to the known crystal structures of materials on database through machine learning. The radial distribution function is used to represent the general characteristics of the known crystal structures, and then the variational autoencoder is employed to generate a set of representative crystal replicas defined in a two-dimensional optimal continuous space. For given chemical compositions and crystal volume, we generate random crystal structures using constraints for crystal symmetry and atomic positions and directly compare their radial distribution functions with those of the known and/or replicated crystals. For selected crystal structures, energy minimization is subsequently performed through first-principles electronic structure calculations. This approach enables us to predict a set of new low-energy crystal structures using only the information on the radial distribution functions of the known structures.