Simulating Spectroscopy Measuring Processes and Results Using Large Language Models

Large language models (LLMs) have recently demonstrated remarkable potential in participating scientific research; however, their reliance on statistical learning limits the ability to perform physically consistent reasoning. To address this challenge, an LLM-Spec framework is presented in this research as an end-to-end tool for experimental planning and result prediction in materials characterization using spectroscopy techniques. LLM-Spec is an LLM-based framework that integrates GPT-4 with established physics, chemistry, and spectroscopy databases and first-principles density functional theory (DFT) simulations. The framework decomposes spectroscopy measurements into four embedded modules, including technique selection, sample preparation, experimental workflow design, and DFT-based spectral prediction. Each module is grounded in structured, physics-informed reasoning logic. By coupling semantic reasoning with physically validated databases and computational models, LLM-Spec transforms qualitative scientific intent into quantitatively verifiable experimental plans and simulation outputs. This integration ensures logical coherence across the spectroscopy measuring process and establishes a scalable paradigm for applying LLMs to data-driven, physically interpretable research in materials science.