Autonomous adaptive spectral analysis with an LLM agent for self-driving laboratories

Yuki K. Wakabayashi; Yui Ogawa

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arXiv · 2610.25619

Autonomous adaptive spectral analysis with an LLM agent for self-driving laboratories

Yuki K. Wakabayashi·Yui Ogawa

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Abstract

Self-driving laboratories accelerate materials exploration through autonomous closed-loop integration of synthesis, characterization, and next-experiment selection. However, characterization does not always directly provide the target property; measured spectra or images often require automated analysis to extract quantitative descriptors based on physically motivated assumptions and predefined criteria. Such procedures can fail for measurements outside these criteria, requiring a decision on whether a reliable value can be recovered or no numerical value should be reported. Here, we implemented an autonomous adaptive spectral-analysis agent using a large language model (LLM) to supervise a deterministic numerical-analysis routine. For each spectrum, the agent executes the predefined analysis script and assesses the result using the measured spectrum and fitting diagnostics. Valid results are accepted unchanged, recoverable failures are reanalyzed through bounded adjustments of predefined fitting parameters, and NaN (not a number) is returned when no scientifically defensible value can be obtained. We evaluated the workflow on optical transmittance spectra from 203 epitaxial beta-Ga2O3 films grown during a previously reported self-driving thin-film growth campaign. The predefined optical analysis script returned Urbach energy, an optical-quality metric, for 183 spectra and failed for 20. The agent retained those 183 values and evaluated 20 exception cases, recovering 16 through bounded parameter adjustments and returning NaN for 4 when the spectral evidence did not support a reliable Urbach tail. The audit records captured spectral features, parameter changes, and rationale for each decision. These results demonstrate autonomous scientific exception handling by an LLM agent while leaving quantitative property extraction to deterministic numerical analysis.

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Yuki K. Wakabayashi, Yui Ogawa. 2026-10-19. Autonomous adaptive spectral analysis with an LLM agent for self-driving laboratories. https://arxiv.org/abs/2610.25619

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