  Generative machine learning models can use data generated by scientific
modeling to create large quantities of novel material structures. Here, we
assess how one state-of-the-art generative model, the physics-guided crystal
generation model (PGCGM), can be used as part of the inverse design process. We
show that the default PGCGM's input space is not smooth with respect to
parameter variation, making material optimization difficult and limited. We
also demonstrate that most generated structures are predicted to be
thermodynamically unstable by a separate property-prediction model, partially
due to out-of-domain data challenges. Our findings suggest how generative
models might be improved to enable better inverse design.
