Artificial Raman Expert: Autonomous Spectroscopic Reasoning Driven by Localized Large Language Models.

Area of Science:

• Analytical Chemistry
• Artificial Intelligence
• Spectroscopy

Background:

• Current automated lab platforms lack real-time heuristic reasoning and dynamic adaptation.
• Existing systems are often limited to rigid synthesis workflows, hindering flexibility.

Purpose of the Study:

• To introduce the Artificial Raman Expert (ARE), an autonomous AI-driven analytical framework.
• To enable real-time heuristic reasoning and dynamic parameter adaptation in analytical sciences.
• To ensure data privacy and reproducibility using localized, open-source large language models (LLMs).

Main Methods:

• Integrated a structured knowledge base (A-Knowhow) to internalize expert decision-making logic.
• Validated ARE's cognitive and optical reasoning in a virtual reality (VR) sandbox before physical deployment.
• Applied ARE to a physical Raman spectrometer for complex physicochemical scenario analysis.

Main Results:

• ARE demonstrated advanced cognitive capabilities in evaluating sample heterogeneity and performing spectral unmixing.
• Achieved efficient trace narcotic isolation from complex matrices within 3 analytical cycles.
• Reduced analytical time and resource consumption by approximately 70% by heuristically halting analyses.
• Dynamically tuned parameters, capping laser exposure to optimize signal-to-noise ratios and prevent phototoxicity.

Conclusions:

• ARE demonstrates the feasibility of context-aware autonomous spectroscopy.
• Provides a foundational proof-of-concept for encoding tacit analytical expertise into AI-driven instrumentation.
• Shows potential for broader application across diverse analytical domains.