  Generative models trained on internet-scale data are capable of generating
novel and realistic texts, images, and videos. A natural next question is
whether these models can advance science, for example by generating novel
stable materials. Traditionally, models with explicit structures (e.g., graphs)
have been used in modeling structural relationships in scientific data (e.g.,
atoms and bonds in crystals), but generating structures can be difficult to
scale to large and complex systems. Another challenge in generating materials
is the mismatch between standard generative modeling metrics and downstream
applications. For instance, common metrics such as the reconstruction error do
not correlate well with the downstream goal of discovering stable materials. In
this work, we tackle the scalability challenge by developing a unified crystal
representation that can represent any crystal structure (UniMat), followed by
training a diffusion probabilistic model on these UniMat representations. Our
empirical results suggest that despite the lack of explicit structure modeling,
UniMat can generate high fidelity crystal structures from larger and more
complex chemical systems, outperforming previous graph-based approaches under
various generative modeling metrics. To better connect the generation quality
of materials to downstream applications, such as discovering novel stable
materials, we propose additional metrics for evaluating generative models of
materials, including per-composition formation energy and stability with
respect to convex hulls through decomposition energy from Density Function
Theory (DFT). Lastly, we show that conditional generation with UniMat can scale
to previously established crystal datasets with up to millions of crystals
structures, outperforming random structure search (the current leading method
for structure discovery) in discovering new stable materials.
