Korean researchers have developed an AI technology for self-driving laboratories (SDLs) that incorporates researchers’ experience and knowledge, expressed in natural language, into autonomous experimental planning.
A research team led by Dr. Gwang-Noh Ahn at the Korea Research Institute of Chemical Technology (KRICT, President Seokmin Shin) developed a human-in-the-loop optimization (HILO) framework that uses large language models (LLMs) to translate researchers’ knowledge into information for experimental optimization.
The technology is expected to reduce costly trial-and-error experiments and accelerate the development of new materials and catalysts.
Self-driving laboratories autonomously identify optimal experimental conditions by learning from previous results. Unlike conventional automated equipment that repeats predefined procedures, SDLs independently select subsequent experiments.
However, conventional SDLs often face a cold start problem, requiring AI to rediscover even well-established chemical knowledge through repeated experiments. This is particularly costly in catalyst research, where synthesis and analysis can take more than half a day per experiment.
To overcome this limitation, the team integrated an LLM with Bayesian optimization (BO). The LLM converts researchers’ natural-language guidance into structured prior information and constraints, allowing AI to incorporate expert knowledge without requiring complex programming or mathematical modeling.
The team also introduced adaptive weight credibility detection (AWCD) to prevent misleading expert knowledge from compromising optimization. AWCD monitors consistency between prior knowledge and experimental data, automatically disabling unreliable priors and reverting to data-driven optimization when sufficient contradictory evidence emerges.
In tests involving deliberately incorrect guidance, conventional methods failed to identify optimal conditions, while the new framework detected misleading information and recovered optimization performance.
Validation using virtual experimental environments based on real data demonstrated substantial improvements. For the Ugi multicomponent reaction, the number of searches required to achieve target performance decreased from approximately 30 to fewer than 10. For electrically conductive P3HT-CNT composites, searches dropped from approximately 20 to just three to five.
These results indicate an effect comparable to having approximately 40 experimental data points available in advance, potentially saving around 20 days of initial exploration in catalyst experiments requiring half a day or more per run.
The team also developed and released an interactive Co-Scientist interface, enabling researchers to adjust experimental directions through natural-language dialogue during optimization. The researchers plan to validate the technology using physical self-driving laboratory equipment.
Dr. Gwang-Noh Ahn of KRICT said, “This technology serves as both a research partner that enables scientists and AI to collaborate and a core software platform functioning as the brain of a self-driving laboratory. We expect it to significantly reduce research and development time and costs.”
The findings were published in the Journal of Chemical Information and Modeling (Impact Factor: 6.4).

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KRICT is a non-profit research institute funded by the Korean government. Since its foundation in 1976, KRICT has played a leading role in advancing national chemical technologies in the fields of chemistry, material science, environmental science, and chemical engineering. Now, KRICT is moving forward to become a globally leading research institute tackling the most challenging issues in the field of Chemistry and Engineering and will continue to fulfill its role in developing chemical technologies that benefit the entire world and contribute to maintaining a healthy planet. More detailed information on KRICT can be found at https://www.krict.re.kr/eng/
This research was supported by the Ministry of Science and ICT through its Strategic Research Program and Nano & Material Technology Development Program, as well as KRICT’s institutional research programs.