AstroSpec-LLM: A Large Language Model Framework for High-throughput Infrared Spectral Prediction of Interstellar PAHs

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ID: 313847
2026
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Abstract
Abstract Polycyclic aromatic hydrocarbons (PAHs) are vital probes of the interstellar medium, but the quartic scaling of traditional quantum chemical calculations hinders the characterization of large species observed by the James Webb Space Telescope (JWST). We present AstroSpec-LLM, a deep-learning framework that adapts the large language model architecture to treat molecular SMILES strings as “chemical sentences” for high-throughput, charge-sensitive spectral prediction. By leveraging a transformer-based encoder enhanced with rotary position embeddings and fine-tuned on a dataset of 24,146 PAH spectra, our model achieves exceptional structural generalization and data efficiency. By bypassing the bottleneck of density functional theory calculations, AstroSpec-LLM enables the rapid synthesis of massive, charge-aware spectral libraries, serving as a useful diagnostic tool for decoding the complex infrared universe.
Reference Key
openalex_W7160702830 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Yuan Liu, Zhao Wang, Dong Qiu
Journal monthly notices of the royal astronomical society
Year 2026
DOI
10.1093/mnras/stag893
URL
https://doi.org/10.1093/mnras/stag893
Keywords Keywords not found

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