Selected work

Representative Papers

Autonomous battery research: Principles of heuristic operando experimentation

Dec 29, 2025

Traditional in situ battery characterization methods struggle to reliably capture stochastic, transient failure events such as dendrite initiation. This work proposes a heuristic in situ experimental framework that integrates physics-informed digital twins with AI agents to actively guide multimodal beamline instrumentation toward mechanistically critical precursors. Departing from conventional uncertainty-driven active learning, the approach innovatively employs entropy-based metrics to quantify scientific information gain, thereby enhancing experimental efficiency and data value while adhering to FAIR data principles. The method effectively mitigates beam-induced damage and data redundancy, successfully capturing transient precursor phenomena overlooked by conventional techniques, and establishes a new paradigm for building trustworthy autonomous battery laboratories.

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Benchmarking Autonomy in Scientific Experiments: A Hierarchical Taxonomy for Autonomous Large-Scale Facilities

Jan 11, 2026

This work addresses the absence of standardized evaluation criteria for autonomous scientific experimentation in large-scale user facilities, where existing taxonomies rely on an owner-operator model that is ill-suited to such environments. The authors propose the BASE scale—a six-level (0–5) autonomy classification framework tailored for these facilities—that introduces “reasoning barrier” (Level 3) as a critical threshold, marking the transition from scalar feedback-based decisions to those enabled by semantic digital twins and time-gated mechanisms. Integrating hierarchical architectures, real-time inference, and time-synchronized technologies, the framework supports zero-shot deployment of intelligent agents. It provides facility managers, funding agencies, and scientists with a standardized metric to assess risk, delineate responsibility, and quantify the degree of intelligence embedded in experimental workflows.

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Recent publications

Latest Papers

Benchmarking Autonomy in Scientific Experiments: A Hierarchical Taxonomy for Autonomous Large-Scale Facilities

Jan 11, 2026

This work addresses the absence of standardized evaluation criteria for autonomous scientific experimentation in large-scale user facilities, where existing taxonomies rely on an owner-operator model that is ill-suited to such environments. The authors propose the BASE scale—a six-level (0–5) autonomy classification framework tailored for these facilities—that introduces “reasoning barrier” (Level 3) as a critical threshold, marking the transition from scalar feedback-based decisions to those enabled by semantic digital twins and time-gated mechanisms. Integrating hierarchical architectures, real-time inference, and time-synchronized technologies, the framework supports zero-shot deployment of intelligent agents. It provides facility managers, funding agencies, and scientists with a standardized metric to assess risk, delineate responsibility, and quantify the degree of intelligence embedded in experimental workflows.

0 citationsRead paper

Autonomous battery research: Principles of heuristic operando experimentation

Dec 29, 2025

Traditional in situ battery characterization methods struggle to reliably capture stochastic, transient failure events such as dendrite initiation. This work proposes a heuristic in situ experimental framework that integrates physics-informed digital twins with AI agents to actively guide multimodal beamline instrumentation toward mechanistically critical precursors. Departing from conventional uncertainty-driven active learning, the approach innovatively employs entropy-based metrics to quantify scientific information gain, thereby enhancing experimental efficiency and data value while adhering to FAIR data principles. The method effectively mitigates beam-induced damage and data redundancy, successfully capturing transient precursor phenomena overlooked by conventional techniques, and establishes a new paradigm for building trustworthy autonomous battery laboratories.

1 citationsRead paper