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Related Experiment Video

Updated: Jul 15, 2026

Synthesizing Amino Acids Modified with Reactive Carbonyls in Silico to Assess Structural Effects Using Molecular Dynamics Simulations

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Adaptive human-in-the-loop optimization using language-guided priors for chemical experiments.

Amirreza Mottafegh¹, Gwang-Noh Ahn¹

¹Digital Chemical Research Center, Korea Research Institute of Chemical Technology, 141 Gajeongro, Yuseong, Daejeon 34114, Republic of Korea.

Journal of Chemical Information and Modeling

|July 13, 2026

Summary

This study introduces a human-in-the-loop framework for self-driving labs, using large language models to incorporate expert knowledge into Bayesian optimization. An adaptive mechanism prevents misleading heuristics, accelerating chemical discovery.

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Area of Science:

Chemical Sciences
Artificial Intelligence
Laboratory Automation

Background:

Self-driving laboratories face efficiency challenges due to the 'cold start' problem, requiring algorithms to relearn known chemical trends.
Existing methods struggle to integrate qualitative expert knowledge into quantitative models and lack safeguards against inaccurate heuristics.

Purpose of the Study:

To develop a human-in-the-loop optimization framework for self-driving laboratories that effectively incorporates expert prior knowledge.
To introduce a mechanism that dynamically adapts to experimental data, mitigating bias and preventing 'prior lock-in' from misleading heuristics.

Main Methods:

Utilized large language models to translate natural language expert prompts into structured priors for Bayesian optimization.
Developed the adaptive weight credibility detection (AWCD) mechanism to evaluate expert hypotheses against experimental data.
Implemented a 'Co-Scientist' interface for iterative knowledge injection and expert guidance of autonomous discovery.

Main Results:

The framework, enriched with accurate expert knowledge, accelerated the identification of high-performing conditions in chemical reactions and material science.
The AWCD mechanism effectively limited performance degradation when initial expert priors were misleading.
Demonstrated accelerated discovery compared to unguided Bayesian optimization in Ugi multicomponent reaction and P3HT-CNT composite datasets.

Conclusions:

The human-in-the-loop framework enhances self-driving laboratory efficiency by integrating expert knowledge via large language models.
The adaptive credibility detection mechanism provides a robust safeguard against misleading expert heuristics, ensuring reliable autonomous discovery.
The interactive interface empowers domain experts to guide AI-driven scientific exploration without requiring specialized computational skills.