Analytical sciences increasingly require autonomous systems capable of real-time heuristic reasoning and dynamic parameter adaptation. However, current automated laboratory platforms remain largely restricted to rigid synthesis workflows. Here, we introduce the Artificial Raman Expert (ARE), an autonomous AI-driven analytical framework powered by localized, open-source large language models (LLMs) to ensure strict data privacy and reproducibility. By integrating a structured knowledge base (A-Knowhow), ARE internalizes the tacit decision-making logic of seasoned spectroscopists. To ensure safe and scalable deployment, ARE's cognitive and optical reasoning was first validated within a risk-free virtual reality (VR) sandbox before being successfully translated to a physical Raman spectrometer. Across highly complex physicochemical scenarios, ARE demonstrated advanced cognitive capabilities: evaluating the spatial heterogeneity of pharmaceutical samples, performing spectral unmixing to isolate trace narcotics from severe forensic matrix interferences within just 3 analytical cycles, heuristically halting analyses after 8-10 iterations-despite human prompts mandating >30 positions-thereby reducing analytical time and resource consumption by ∼70%. Additionally, ARE conducted constraint-aware parameter tuning, dynamically capping laser exposure at 60 s to optimize signal-to-noise ratios while strictly preventing biological cell phototoxicity. By actively optimizing analytical resources and adapting to noisy data, ARE demonstrates the feasibility of context-aware autonomous spectroscopy. This work provides a foundational proof-of-concept for encoding tacit analytical expertise into AI-driven instrumentation, with potential for broader application across diverse analytical domains.
Lim et al. (Fri,) studied this question.