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.

使用物理引导的晶体生成模型（PGCGM）来作为逆向设计过程的一部分，评估生成式机器学习模型如何利用科学建模生成的数据来创造大量的新材料结构。研究发现，PGCGM 的输入空间对参数变化并不平滑，导致了材料优化的困难和局限性；同时，通过一个独立的属性预测模型表明，大部分生成的结构在热力学上是不稳定的，部分原因是由于领域外数据挑战。研究结果为改进生成模型以实现更好的逆向设计提供了建议。