ORGANA: A robotic assistant for automated chemistry experimentation and characterization

IF 17.3 1区 材料科学 Q1 MATERIALS SCIENCE, MULTIDISCIPLINARY
Matter Pub Date : 2024-11-12 DOI:10.1016/j.matt.2024.10.015
Kourosh Darvish, Marta Skreta, Yuchi Zhao, Naruki Yoshikawa, Sagnik Som, Miroslav Bogdanovic, Yang Cao, Han Hao, Haoping Xu, Alán Aspuru-Guzik, Animesh Garg, Florian Shkurti
{"title":"ORGANA: A robotic assistant for automated chemistry experimentation and characterization","authors":"Kourosh Darvish, Marta Skreta, Yuchi Zhao, Naruki Yoshikawa, Sagnik Som, Miroslav Bogdanovic, Yang Cao, Han Hao, Haoping Xu, Alán Aspuru-Guzik, Animesh Garg, Florian Shkurti","doi":"10.1016/j.matt.2024.10.015","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":388,"journal":{"name":"Matter","volume":null,"pages":null},"PeriodicalIF":17.3000,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Matter","FirstCategoryId":"88","ListUrlMain":"https://doi.org/10.1016/j.matt.2024.10.015","RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATERIALS SCIENCE, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0

Abstract

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.

Abstract Image

ORGANA:用于自动化化学实验和表征的机器人助手
化学实验可能是资源和劳动力密集型的,通常需要手工操作,如电化学中的电极抛光。传统的实验室自动化基础设施在适应新实验方面面临挑战。为了解决这个问题,我们推出了 ORGANA,这是一种辅助机器人系统,可利用决策和感知工具自动完成各种化学实验。它与化学家共同决策,控制机器人和实验室设备。ORGANA 使用大型语言模型 (LLM) 与化学家互动,以推导实验目标、处理歧义并提供实验日志。ORGANA 利用视觉反馈计划和执行复杂任务,同时支持调度和并行任务执行。我们展示了 ORGANA 在溶解度、pH 值测量、重结晶和电化学实验中的能力。在电化学实验中,ORGANA 可并行执行 19 步计划,以鉴定液流电池中醌衍生物的特性。我们的用户研究表明,ORGANA 可减少 50% 以上的挫折感和体力需求,用户在使用 ORGANA 时平均可节省 80.3% 的时间。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Matter
Matter MATERIALS SCIENCE, MULTIDISCIPLINARY-
CiteScore
26.30
自引率
2.60%
发文量
367
期刊介绍: Matter, a monthly journal affiliated with Cell, spans the broad field of materials science from nano to macro levels,covering fundamentals to applications. Embracing groundbreaking technologies,it includes full-length research articles,reviews, perspectives,previews, opinions, personnel stories, and general editorial content. Matter aims to be the primary resource for researchers in academia and industry, inspiring the next generation of materials scientists.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信