Autonomous materials discovery with desired properties is one of the ultimate goals for modern materials science, and the current studies have been focusing mostly on high-throughput screening based on density functional theory calculations and forward modelling of physical properties using machine learning. Applying the deep learning techniques, we have developed a generative model which can predict distinct stable crystal structures by optimizing the formation energy in the latent space. It is demonstrated that the optimization of physical properties can be integrated into the generative model as on-top screening or backwards propagator, both with their own advantages.

Applying the generative models on the binary Bi-Se system reveals that distinct crystal structures can be obtained covering the whole composition range, and the phases on the convex hull can be reproduced after the generated structures are fully relaxed to the equilibrium. And by learning from the Materials Project database, the method generates a wide range of thermodynamically stable structures that do not exist in the database, covering intermetallic compounds, oxides, Li-containing compounds and chalcogenides, etc.

It is further demonstrated that increasing the number of structures learnt, or adding constrained models, can further improve the performance of the model. Thus, this model can pave the way to achieve the inverse design of materials with optimal properties.