  We developed an inverse design framework enabling automated generation of
stable multi-component crystal structures by optimizing the formation energies
in the latent space based on reversible crystal graphs with continuous
representation. It is demonstrated that 9,160 crystal structures can be
generated out of 50,000 crystal graphs, leading to 8,310 distinct cases using a
training set of 52,615 crystal structures from Materials Project. Detailed
analysis on 15 selected systems reveals that unreported crystal structures
below the convex hull can be discovered in 6 material systems. Moreover, the
generation efficiency can be further improved by considering extra hypothetical
structures in the training. This paves the way to perform inverse design of
multicomponent materials with possible multi-objective optimization.
