4 citations · 7 across the 5 of their papers we have counts for
5 papers
Generating Symmetric Materials using Latent Flow Matching
Anmar Karmush, Cedric Mathieu Brandenburg, Soheil Ershadrad +3
Tackling the task of materials generation, we aim to enhance the previously proposed All-atom Diffusion Transformer (ADiT) by introducing SymADiT, a symmetry-aware variant. To do s…
WyckoffDiff -- A Generative Diffusion Model for Crystal Symmetry
Filip Ekström Kelvinius, Oskar B. Andersson, Abhijith S. Parackal +3
Crystalline materials often exhibit a high level of symmetry. However, most generative models do not account for symmetry, but rather model each atom without any constraints on its…
Solving Linear-Gaussian Bayesian Inverse Problems with Decoupled Diffusion Sequential Monte Carlo
Filip Ekström Kelvinius, Zheng Zhao, Fredrik Lindsten
A recent line of research has exploited pre-trained generative diffusion models as priors for solving Bayesian inverse problems. We contribute to this research direction by designi…
Discriminator Guidance for Autoregressive Diffusion Models
Filip Ekström Kelvinius, Fredrik Lindsten
We introduce discriminator guidance in the setting of Autoregressive Diffusion Models. The use of a discriminator to guide a diffusion process has previously been used for continuo…
Accelerating Molecular Graph Neural Networks via Knowledge Distillation
Filip Ekström Kelvinius, Dimitar Georgiev, Artur Petrov Toshev +1
Recent advances in graph neural networks (GNNs) have enabled more comprehensive modeling of molecules and molecular systems, thereby enhancing the precision of molecular property p…