  The constant demand for new functional materials calls for efficient
strategies to accelerate the materials design and discovery. In addressing this
challenge, machine learning generative models can offer promising opportunities
since they allow for the continuous navigation of chemical space via low
dimensional latent spaces. In this work, we employ a crystal representation
that is inversion-free with a low memory requirement based on unit cell
information and fractional atomic coordinates, and build the generative
adversarial network (GAN) for crystal structures. The proposed model is then
applied to the Mg-Mn-O ternary inorganic materials system to generate novel
structures with application as potential water-splitting photoanodes, and
combined with the evaluation of their photoanode properties for high-throughput
virtual screening (HTVS). The generative-HTVS system that we built predicts 23
new crystal structures with a reasonable predicted stability and bandgap. These
findings suggest that the proposed generative model can be an effective way to
explore hidden portions of the chemical space, an area that is usually
unreachable when conventional substitution-based discovery is employed.

Materials Science;Computational Physics