1,026 research outputs found
Crystal Structure Prediction by Joint Equivariant Diffusion
Crystal Structure Prediction (CSP) is crucial in various scientific
disciplines. While CSP can be addressed by employing currently-prevailing
generative models (e.g. diffusion models), this task encounters unique
challenges owing to the symmetric geometry of crystal structures -- the
invariance of translation, rotation, and periodicity. To incorporate the above
symmetries, this paper proposes DiffCSP, a novel diffusion model to learn the
structure distribution from stable crystals. To be specific, DiffCSP jointly
generates the lattice and atom coordinates for each crystal by employing a
periodic-E(3)-equivariant denoising model, to better model the crystal
geometry. Notably, different from related equivariant generative approaches,
DiffCSP leverages fractional coordinates other than Cartesian coordinates to
represent crystals, remarkably promoting the diffusion and the generation
process of atom positions. Extensive experiments verify that our DiffCSP
significantly outperforms existing CSP methods, with a much lower computation
cost in contrast to DFT-based methods. Moreover, the superiority of DiffCSP is
also observed when it is extended for ab initio crystal generation
Autoregressive Diffusion Model for Graph Generation
Diffusion-based graph generative models have recently obtained promising
results for graph generation. However, existing diffusion-based graph
generative models are mostly one-shot generative models that apply Gaussian
diffusion in the dequantized adjacency matrix space. Such a strategy can suffer
from difficulty in model training, slow sampling speed, and incapability of
incorporating constraints. We propose an \emph{autoregressive diffusion} model
for graph generation. Unlike existing methods, we define a node-absorbing
diffusion process that operates directly in the discrete graph space. For
forward diffusion, we design a \emph{diffusion ordering network}, which learns
a data-dependent node absorbing ordering from graph topology. For reverse
generation, we design a \emph{denoising network} that uses the reverse node
ordering to efficiently reconstruct the graph by predicting the node type of
the new node and its edges with previously denoised nodes at a time. Based on
the permutation invariance of graph, we show that the two networks can be
jointly trained by optimizing a simple lower bound of data likelihood. Our
experiments on six diverse generic graph datasets and two molecule datasets
show that our model achieves better or comparable generation performance with
previous state-of-the-art, and meanwhile enjoys fast generation speed.Comment: 18 page
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