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Autonomy in materials research: a case study in carbon nanotube growth (自主材料研究:以碳纳米管生长为例)
发布时间:2016-11-11

Autonomy in materials research: a case study in carbon nanotube growth (自主材料研究:以碳纳米管生长为例)

Pavel Nikolaev, Daylond Hooper, Frederick Webber, Rahul Rao, Kevin Decker, Michael Krein,Jason Poleski, Rick Barto & Benji Maruyama
npj Computational Materials
 2, Article number: 16031 (2016)
doi:10.1038/npjcompumats.2016.31
Published online:21 October 2016
Abstract| Full Text | PDF OPEN
摘要:
材料进步是我们技术进步的重要因素,然而,材料的发现和开发过程却步履蹒跚。我们目前的研究过程是以人为中心的,由人类的研究人员来设计、执行、分析和解释实验结果,然后再确定下一步该做什么。本研究在国际上首次建立了一个自主研究系统(ARES),即一种能够自主研发的机器人,可做闭环迭代式材料实验。ARES能利用自主机器人、人工智能、数据科学、高通量和原位技术方面的诸多进展,并能够设计、执行和分析其自己所做的实验,比目前的研究方法速度快几个数量级。本研究将ARES投入单壁碳纳米管的合成研究,结果显示,ARES成功地学会、并以预定速度生长单壁碳纳米管。ARES未来将广泛参与以人为中心的科研、自主研发机器人、人-机合作等领域的工作。我们认为,诸如ARES之类的自主研发机器人,将构成人类理解能力的颠覆性进步,并以前所未有的速度研发复杂材料。

Abstract:Advances in materials are an important contributor to our technological progress, and yet the process of materials discovery and development itself is slow. Our current research process is human-centred, where human researchers design, conduct, analyse and interpret experiments, and then decide what to do next. We have built an Autonomous Research System (ARES)—an autonomous research robot capable of first-of-its-kind closed-loop iterative materials experimentation. ARES exploits advances in autonomous robotics, artificial intelligence, data sciences, and high-throughput and in situ techniques, and is able to design, execute and analyse its own experiments orders of magnitude faster than current research methods. We applied ARES to study the synthesis of single-walled carbon nanotubes, and show that it successfully learned to grow them at targeted growth rates. ARES has broad implications for the future roles of humans and autonomous research robots, and for human-machine partnering. We believe autonomous research robots like ARES constitute a disruptive advance in our ability to understand and develop complex materials at an unprecedented rate.

Editorial Summary

Autonomy: Robot Researchers Build Better Nanotubes (自主性:机器人研究者造出更好的纳米管)

一个能够使用反馈算法学习如何优化碳纳米管产物的机器人,有可能成为材料研发人员的得力合作伙伴。美国空军研究实验室和洛克希德马丁公司先进技术实验室的一组科学家建立了一个自主的研究系统(ARES),即一种可做迭代学习循环的人工智能机器人,它能自主设计、执行和分析其自己所做的实验,速度比目前的研究方法快几个数量级。与人类科研人员组成团队,它学会了按照一整套条件来调控碳纳米管以预定速度生长。该研究创建了一个快速、高通量的方法,将广泛适用于其他材料的研究,也将使研究人员能做更具挑战性的实验研究工作。

A robot that learns how to optimize carbon nanotube production using feedback algorithms may be a valuable partner in materials discovery. A team of scientists at the Air Force Research Laboratory and Lockheed Martin Advanced Technology Laboratories have built an Autonomous Research System (ARES), a robot guided by artificial intelligence in an iterative learning loop, that is capable of designing, executing and analyzing its own experiments orders of magnitude faster than current research methods. Teaming with human researchers, ARES learned to control the growth of carbon nanotubes by converging on a set of conditions that yielded an objective growth rate. This approach creates a rapid, high-throughput approach that is broadly applicable to other materials research problems and enables researchers to pursue more challenging experimental campaigns.

 
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