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This work proposes SpecXMaster, an intelligent framework leveraging Agentic Reinforcement Learning (RL) for NMR molecular spectral interpretation that enables automated extraction of multiplicity information from both 1H and 13C spectra directly from raw FID (free induction decay) data.
Nuclear Magnetic Resonance spectroscopy is the gold standard for molecular structure elucidation, yet interpreting complex spectra for unknown molecules remains a bottleneck reliant on human expertise, so NMRAgent establishes a new paradigm for interpretable AI in analytical chemistry.
Uni-XAS is presented, a unified benchmark and learning framework that reframes bidirectional XAS modeling as a cross-modal alignment and conditional generation problem, and introduces Permutation-Rectified Flow Matching, which integrates type-wise optimal transport into a continuous generative flow to provide a princip...
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