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ORGANA: A Robotic Assistant for Automated Chemistry Experimentation and Characterization

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arxiv 2401.06949 v2 pith:LVMQEKXJ submitted 2024-01-13 cs.RO cs.AI

classification cs.ROcs.AI
keywords organaexperimentschemistryelectrochemistrychemistsexecutesexperimentparallel
verification ladder T0 review T1 audit T2 compute T3 formal
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Chemistry experiments can be resource- and labor-intensive, often requiring manual tasks like polishing electrodes in electrochemistry. Traditional lab automation infrastructure faces challenges adapting to new experiments. To address this, we introduce ORGANA, an assistive robotic system that automates diverse chemistry experiments using decision-making and perception tools. It makes decisions with chemists in the loop to control robots and lab devices. ORGANA interacts with chemists using Large Language Models (LLMs) to derive experiment goals, handle disambiguation, and provide experiment logs. ORGANA plans and executes complex tasks with visual feedback, while supporting scheduling and parallel task execution. We demonstrate ORGANA's capabilities in solubility, pH measurement, recrystallization, and electrochemistry experiments. In electrochemistry, it executes a 19-step plan in parallel to characterize quinone derivatives for flow batteries. Our user study shows ORGANA reduces frustration and physical demand by over 50%, with users saving an average of 80.3% of their time when using it.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 20 citations worldwide. Full citation record

  1. Large Language Models Transform Organic Synthesis From Reaction Prediction to Automation

    cs.AI 2025-08 reject novelty 2.0 of 10

    This survey claims to be the first systematic review of LLMs for organic synthesis, but its central 'evaluation' is never actually performed.

  2. Perspective on Utilizing Foundation Models for Laboratory Automation in Materials Research

    cs.RO 2025-06 accept novelty 1.0 of 10

    A perspective article reviews the state of using foundation models for laboratory automation and proposes a roadmap for fully autonomous experiments.

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