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20242026
most citedFlowMM: Generating Materials with Riemannian Flow Matching

9 citations · 17 across the 8 of their papers we have counts for

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5 papers · 1 filter

cs.LG2025

Space Group Conditional Flow Matching

Omri Puny, Yaron Lipman, Benjamin Kurt Miller

Inorganic crystals are periodic, highly-symmetric arrangements of atoms in three-dimensional space. Their structures are constrained by the symmetry operations of a crystallographi…

cs.LG20251 cited

Adjoint Sampling: Highly Scalable Diffusion Samplers via Adjoint Matching

Aaron Havens, Benjamin Kurt Miller, Bing Yan +10

We introduce Adjoint Sampling, a highly scalable and efficient algorithm for learning diffusion processes that sample from unnormalized densities, or energy functions. It is the fi…

cs.LG2025

All-atom Diffusion Transformers: Unified generative modelling of molecules and materials

Chaitanya K. Joshi, Xiang Fu, Yi-Lun Liao +4

Diffusion models are the standard toolkit for generative modelling of 3D atomic systems. However, for different types of atomic systems -- such as molecules and materials -- the ge…

cs.LG20247 cited

FlowLLM: Flow Matching for Material Generation with Large Language Models as Base Distributions

Anuroop Sriram, Benjamin Kurt Miller, Ricky T. Q. Chen +1

Material discovery is a critical area of research with the potential to revolutionize various fields, including carbon capture, renewable energy, and electronics. However, the imme…

cs.LG20249 cited

FlowMM: Generating Materials with Riemannian Flow Matching

Benjamin Kurt Miller, Ricky T. Q. Chen, Anuroop Sriram +1

Crystalline materials are a fundamental component in next-generation technologies, yet modeling their distribution presents unique computational challenges. Of the plausible arrang…