RamanGPT
Bidirectional ML between crystal structures and Raman spectra
RamanGPT establishes a bidirectional mapping between crystal structures and Raman spectra. A forward model, a graph neural network (ALIGNN), predicts a Raman spectrum directly from a crystal structure. An inverse workflow recovers structure from a spectrum by matching it against a computational Raman database (cosine similarity) and generating candidate structures with a fine-tuned generative transformer (AtomGPT), which are then relaxed with an ML force field (ALIGNN-FF). Trained on a database of ~5,000 first-principles (DFPT) spectra, the goal is to make Raman a faster, more quantitative structural probe.
This work is described in RamanGPT: Bidirectional Mapping Between Crystal Structures and Raman Spectra with Graph Neural Networks and Generative Transformers (arXiv:2606.03764).
Demo: atomgpt.org/raman