22 citations · 33 across the 5 of their papers we have counts for
5 papers
Generative Pseudo-Force Fields for Molecular Generation
Stefaan Simon Pierre Hessmann, Khaled Kahouli, Stefan Gugler +4
Generating stable molecular conformations typically forces a tradeoff between the physical realism of energy-based relaxation and the sampling efficiency of data-driven generative…
Fast protein backbone generation with SE(3) flow matching
Jason Yim, Andrew Campbell, Andrew Y. K. Foong +8
We present FrameFlow, a method for fast protein backbone generation using SE(3) flow matching. Specifically, we adapt FrameDiff, a state-of-the-art diffusion model, to the flow-mat…
Reaction coordinate flows for model reduction of molecular kinetics
Hao Wu, Frank Noé
In this work, we introduce a flow based machine learning approach, called reaction coordinate (RC) flow, for discovery of low-dimensional kinetic models of molecular systems. The R…
Navigating the Design Space of Equivariant Diffusion-Based Generative Models for De Novo 3D Molecule Generation
Tuan Le, Julian Cremer, Frank Noé +2
Deep generative diffusion models are a promising avenue for 3D de novo molecular design in materials science and drug discovery. However, their utility is still limited by suboptim…
Electronic excited states in deep variational Monte Carlo
Mike Entwistle, Zeno Schätzle, Paolo A. Erdman +2
Obtaining accurate ground and low-lying excited states of electronic systems is crucial in a multitude of important applications. One ab initio method for solving the Schrödinger e…