Autonomy in materials research: a case study in carbon nanotube growth
Source abstract
Abstract
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.
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Research context
OpenAlex topics: Machine Learning in Materials Science · Advanced Memory and Neural Computing · Nanowire Synthesis and Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (45)
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