3 papers
cs.LG2026
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…
cs.LG2026
OMatG-flash: An All-Atom Flow Map with Reinforce Adjoint Matching for Scalable Materials Discovery
Thomas Egg, Harry Winston Sullivan, Ellad B. Tadmor +1
The discovery of novel inorganic materials drives technological breakthroughs in critical fields such as computing and energy storage. Generative AI has promised to accelerate the…
cs.LG2026
MolCrystalFlow: Molecular Crystal Structure Prediction via Flow Matching
Cheng Zeng, Harry W. Sullivan, Thomas Egg +8
Molecular crystal structure prediction represents a grand challenge in computational chemistry due to large sizes of constituent molecules and complex intra- and intermolecular int…