  The design of functional materials with desired properties is essential in
driving technological advances in areas like energy storage, catalysis, and
carbon capture. Generative models provide a new paradigm for materials design
by directly generating entirely novel materials given desired property
constraints. Despite recent progress, current generative models have low
success rate in proposing stable crystals, or can only satisfy a very limited
set of property constraints. Here, we present MatterGen, a model that generates
stable, diverse inorganic materials across the periodic table and can further
be fine-tuned to steer the generation towards a broad range of property
constraints. To enable this, we introduce a new diffusion-based generative
process that produces crystalline structures by gradually refining atom types,
coordinates, and the periodic lattice. We further introduce adapter modules to
enable fine-tuning towards any given property constraints with a labeled
dataset. Compared to prior generative models, structures produced by MatterGen
are more than twice as likely to be novel and stable, and more than 15 times
closer to the local energy minimum. After fine-tuning, MatterGen successfully
generates stable, novel materials with desired chemistry, symmetry, as well as
mechanical, electronic and magnetic properties. Finally, we demonstrate
multi-property materials design capabilities by proposing structures that have
both high magnetic density and a chemical composition with low supply-chain
risk. We believe that the quality of generated materials and the breadth of
MatterGen's capabilities represent a major advancement towards creating a
universal generative model for materials design.
