Ziyu Xiong will present her General Exam "Atomic Diffusion Models for Small Molecule Structure Elucidation from NMR Spectra" on Friday, April 17, 2026 at 3:00 PM in Friend 202 and via zoom. Zoom link: https://princeton.zoom.us/j/95476730233 Committee Members: Ellen Zhong (advisor), Zhuang Liu, Ryan Adams Abstract: Nuclear Magnetic Resonance (NMR) spectroscopy is a cornerstone technique for determining the structures of small molecules and is especially critical in the discovery of novel natural products and clinical therapeutics. Yet, interpreting NMR spectra remains a time-consuming, manual process requiring extensive domain expertise. We introduce ChefNMR (CHemical Elucidation From NMR), an end-to-end framework that directly predicts an unknown molecule's structure solely from its 1D NMR spectra and chemical formula. We frame structure elucidation as conditional generation from an atomic diffusion model built on a non-equivariant transformer architecture. To model the complex chemical groups found in natural products, we curated a dataset of simulated 1D NMR spectra for over 111,000 natural products. ChefNMR predicts the structures of challenging natural product compounds with an unsurpassed accuracy of over 65% on simulated benchmarks, but generalizing to real experimental NMR spectra remains a key challenge. To bridge this synthetic-to-real gap, we propose further improvements, including a more robust peak-based spectral encoder, a flow matching generative framework, a multimodal transformer architecture, and expanded training data. This work takes a significant step toward solving the grand challenge of automating small-molecule structure elucidation and highlights the potential of deep learning in accelerating molecular discovery. Reading List: https://docs.google.com/document/d/1OLM7gak0-XaH8To0Al_zOrEIwyvezDM4bAxvUyOi... Everyone is invited to attend the talk, and those faculty wishing to remain for the oral exam following are welcome to do so.