AI RESEARCH
Generative Pseudo-Force Fields for Molecular Generation
arXiv CS.LG
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ArXi:2605.19050v1 Announce Type: new Generating stable molecular conformations typically forces a tradeoff between the physical realism of energy-based relaxation and the sampling efficiency of data-driven generative models. While machine learning force fields (MLFFs) can sample stable conformations by relaxing molecular geometries according to physical forces, they require costly ab-initio