  Recent advances in deep learning have enabled the generation of realistic
data by training generative models on large datasets of text, images, and
audio. While these models have demonstrated exceptional performance in
generating novel and plausible data, it remains an open question whether they
can effectively accelerate scientific discovery through the data generation and
drive significant advancements across various scientific fields. In particular,
the discovery of new inorganic materials with promising properties poses a
critical challenge, both scientifically and for industrial applications.
However, unlike textual or image data, materials, or more specifically crystal
structures, consist of multiple types of variables - including lattice vectors,
atom positions, and atomic species. This complexity in data give rise to a
variety of approaches for representing and generating such data. Consequently,
the design choices of generative models for crystal structures remain an open
question. In this study, we explore a new type of diffusion model for the
generative inverse design of crystal structures, with a backbone based on a
Transformer architecture. We demonstrate our models are superior to previous
methods in their versatility for generating crystal structures with desired
properties. Furthermore, our empirical results suggest that the optimal
conditioning methods vary depending on the dataset.
