The Kitchin Research Group

In our new paper published in Nature Machine Intelligence, my colleagues Hongliang Xin, Heather Kulik, and I explore what we call "agentic science" – a new paradigm where AI agents can (semi-)autonomously conduct scientific research.

Scientific discovery has evolved through distinct eras: from early empirical observations and theoretical frameworks like Newtonian mechanics, through the computational modeling revolution, to today's data science approaches. We argue that we're now entering the age of agentic science, where AI systems don't just analyze data but can independently reason, plan experiments, and interact with both digital tools and physical laboratory equipment.

What makes these AI agents special is their capacity for independent agency. Built around large language models that can process text, images, and structured data, they can actively learn, integrate with external tools, and think strategically about long-term research goals. Systems like Coscientist can already interpret natural language requests and autonomously operate lab equipment, while A-Lab represents a fully autonomous materials synthesis laboratory.

However, we're honest about the challenges. These systems can "hallucinate" – producing convincing but incorrect information – and they're sensitive to how questions are phrased. We also lack standardized ways to evaluate their performance, and they consume significant computational resources.

The key to success lies in maintaining human oversight while leveraging AI for high-throughput tasks. With proper safeguards, transparent documentation, and ethical considerations, agentic AI could dramatically accelerate scientific discovery while actually improving reproducibility by systematically analyzing literature and identifying research gaps.

We believe this represents a fundamental shift in how science gets done – not replacing human scientists, but creating powerful human-AI partnerships that could unlock new pathways to discovery.

@article{xin-2025-towar-agent,
  author =       {Hongliang Xin and John R. Kitchin and Heather J. Kulik},
  title =        {Towards Agentic Science for Advancing Scientific Discovery},
  journal =      {Nature Machine Intelligence},
  volume =       {nil},
  number =       {nil},
  pages =        {nil},
  year =         2025,
  doi =          {10.1038/s42256-025-01110-x},
  url =          {https://doi.org/10.1038/s42256-025-01110-x},
  DATE_ADDED =   {Tue Sep 16 13:36:03 2025},
}

Copyright (C) 2025 by John Kitchin. See the License for information about copying.

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