1 citations · 1 across the 2 of their papers we have counts for
4 papers
Riemannian Flow Models with Reinforcement Learning for Molecular Crystal Structure Prediction
Thomas Egg, Harry Winston Sullivan, Maya M. Martirossyan +8
Crystal structure governs material properties, making crystal structure prediction (CSP) a fundamental problem in materials science. Generative models are a promising approach for…
Animator-Centric Skeleton Generation on Objects with Fine-Grained Details
Mingze Sun, Cheng Zeng, Jiansong Pei +7
Skeleton generation is essential for animating 3D assets, but current deep learning methods remain limited: they cannot handle the growing structural complexity of modern models an…
MolGuidance: Advanced Guidance Strategies for Conditional Molecular Generation with Flow Matching
Jirui Jin, Cheng Zeng, Pawan Prakash +5
Key objectives in conditional molecular generation include ensuring chemical validity, aligning generated molecules with target properties, promoting structural diversity, and enab…
PropMolFlow: Property-Guided Molecule Generation with Geometry-Complete Flow Matching
Cheng Zeng, Jirui Jin, Connor Ambrose +7
Molecule generation is advancing rapidly in chemical discovery and drug design. Flow matching methods have recently set the state of the art (SOTA) in unconditional molecule genera…