65 citations · 70 across the 5 of their papers we have counts for
9 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…
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…
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…
Guided Diffusion for the Discovery of New Superconductors
Pawan Prakash, Jason B. Gibson, Zhongwei Li +13
The inverse design of materials with specific desired properties, such as high-temperature superconductivity, represents a formidable challenge in materials science due to the vast…
All that structure matches does not glitter
Maya M. Martirossyan, Thomas Egg, Philipp Hoellmer +7
Generative models for materials, especially inorganic crystals, hold potential to transform the theoretical prediction of novel compounds and structures. Advancement in this field…
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…
Open Materials Generation with Stochastic Interpolants
Philipp Hoellmer, Thomas Egg, Maya M. Martirossyan +11
The discovery of new materials is essential for enabling technological advancements. Computational approaches for predicting novel materials must effectively learn the manifold of…
The need to implement FAIR principles in biomolecular simulations
Rommie Amaro, Johan Åqvist, Ivet Bahar +125
This letter illustrates the opinion of the molecular dynamics (MD) community on the need to adopt a new FAIR paradigm for the use of molecular simulations. It highlights the necess…
Guided Multi-objective Generative AI to Enhance Structure-based Drug Design
Amit Kadan, Kevin Ryczko, Erika Lloyd +2
Generative AI has the potential to revolutionize drug discovery. Yet, despite recent advances in deep learning, existing models cannot generate molecules that satisfy all desired p…