Periodic Materials Generation using Text-Guided Joint Diffusion Model
KISHALAY DAS Subhojyoti Khastagir Pawan Goyal Seung-Cheol Lee Satadeep Bhattacharjee Niloy Ganguly
Equivariant diffusion models have emerged as the prevailing approach for generat-
ing novel crystal materials due to their ability to leverage the physical symmetries
of periodic material structures. However, current models do not effectively learn the
joint distribution of atom types, fractional coordinates, and lattice structure of the
crystal material in a cohesive end-to-end diffusion framework. Also, none of these
models work under realistic setups, where users specify the desired characteristics
that the generated structures must match. In this work, we introduce TGDMat, a
novel text-guided diffusion model designed for 3D periodic material generation.
Our approach integrates global structural knowledge through textual descriptions
at each denoising step while jointly generating atom coordinates, types, and lattice
structure using a periodic-E(3)-equivariant graph neural network (GNN). Extensive
experiments using popular datasets on benchmark tasks reveal that TGDMat out-
performs existing baseline methods by a good margin. Notably, for the structure
prediction task, with just one generated sample, TGDMat outperforms all baseline
models, highlighting the importance of text-guided diffusion. Further, in the genera-
tion task, TGDMat surpasses all baselines and their text-fusion variants, showcasing
the effectiveness of the joint diffusion paradigm. Additionally, incorporating textual
knowledge reduces overall training and sampling computational overhead while
enhancing generative performance when utilizing real-world textual prompts from
experts. Code is available at https://github.com/kdmsit/TGDMat
ML4Materials, Diffusion Models, Crystal Materials, Material Generation, AI4Science
We introduce a novel text-guided diffusion model for generating periodic materials. Our model jointly produces atom fractional coordinates, types, and lattice structures using a periodic E(3)-equivariant graph neural network (GNN).