Closed-Loop Molecular Design with Calibrated Deference

2026-05-27

AI Summary

Traditional AI-driven molecular design systems struggle to recognize when their underlying assumptions or computational tools fail and lack the capability to actively guide experimental correction. This work proposes CLIO, a cognitive closed-loop agent that integrates dynamic belief graphs, recursive plan–execute cycles, and a “calibrated concession” mechanism to enable the AI to autonomously diagnose model limitations, generate mechanistic hypotheses, and direct experimental validation within a human–AI collaborative framework. By tightly coupling computational predictions with experimental feedback, CLIO successfully designed novel anolyte molecules exhibiting a 90–130 mV improvement in redox potential and resolved irreversibility issues through mechanistic analysis, thereby completing a full design–synthesis–testing–optimization cycle.

Abstract

We present Cognitive Loop via In-Situ Optimization (CLIO), an agent that couples a continuously-updated belief-state graph with a recursive plan-then-act loop. The result is a reasoning agent that can contribute something qualitatively different, which we term \emph{calibrated deference}: the capacity to recognize when its own tools or assumptions are failing, to adapt its strategy in response, and to generate mechanistic hypotheses that guide experimental revision. We tested CLIO in a closed-loop human-AI campaign to design an aqueous organic redox flow battery (AORFB) negolyte, with CLIO leading proposal and interpretation in close partnership with chemists who synthesized, characterized, and weighed in on design choices. Across 17 candidates over three rounds, CLIO converged on a top phosphonate candidate; characterization confirmed a 130~mV improvement in redox potential over the literature baseline. Characterization then revealed unexpectedly poor electrochemical reversibility -- a regression no property predictor had flagged. CLIO generated competing mechanistic hypotheses, prioritized discriminating diagnostics, traced the failure to phosphonate-potassium ion pairing, and prescribed a sulfonate replacement. The resulting compound showed substantially improved electrochemical reversibility and maintained a 90~mV improvement in redox potential, closing the design-make-test-redesign loop.

Problem

• closed-loop molecular design
• calibrated deference
• mechanistic hypothesis
• redox flow battery
• AI-chemist collaboration

Innovation

• calibrated deference
• closed-loop molecular design
• belief-state graph
• mechanistic hypothesis generation
• human-AI collaboration