Discovery 多智能体系统以校准置信度完成闭环分子设计实验

Authors:Newman Cheng, Gordon Broadbent IV, Jason Dong, Syed Mohammed Ali Hussaini, Farman Ullah, Morris Sharp, Gabrielle Barnes, Nanlin Guo, Deyu Zou, Eliot F. Woods, Jong-Hwa Shon, Yangang Liang, Ruozhu Feng, Wei Wang, Karin Strauss, William Chappell, David G. Kwabi, Bichlien H. Nguyen, Jake A. Smith

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Abstract:As AI systems become increasingly capable scientific collaborators, a fundamental challenge that arises is not only generating explanations, but calibrating confidence in them. We present Discovery, a multi-agent scientific system designed to assess the reliability of tools and data during adaptive reasoning, track evidence and assumptions while resolving uncertainty using a persistent graph memory. Discovery enables a capability we term calibrated deference: the ability to adjust confidence in its tool use, assumptions, and mechanistic explanations based on these assessments, communicate uncertainty to human collaborators, and adapt strategies accordingly. We evaluated Discovery in a closed-loop human-AI campaign to design an aqueous organic redox flow battery negative electrolyte. Discovery proposed candidates and interpreted results in collaboration with chemists, who synthesized, characterized, and guided design. Across 17 candidates over three rounds, Discovery converged on a candidate with a redox potential improved by 130 mV relative to the literature baseline. However, experimental characterization revealed poor electrochemical reversibility that the predictors used in the campaign could not anticipate. In response, Discovery generated competing mechanistic hypotheses, prioritized discriminating diagnostics, and traced the behavior to phosphonate-potassium ion pairing. Discovery designed a sulfonate analog to reduce potassium-ion pairing, and the resulting compound improved electrochemical reversibility while maintaining a 90 mV improvement in redox potential, closing the design-make-test-redesign loop. Across the campaign, Discovery demonstrated the ability to maintain a coherent world model, updating in response to unexpected results, and leverage it to drive molecular design.

Subjects:	Computational Engineering, Finance, and Science (cs.CE); Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA); Chemical Physics (physics.chem-ph)
Cite as:	arXiv:2606.02618 [cs.CE]
	(or arXiv:2606.02618v2 [cs.CE] for this version)
	https://doi.org/10.48550/arXiv.2606.02618 arXiv-issued DOI via DataCite

Submission history

From: Jake Smith [view email]
[v1] Wed, 27 May 2026 16:44:34 UTC (11,190 KB)
[v2] Tue, 10 Nov 2026 19:03:31 UTC (13,251 KB)