The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Autonomy in materials research: a case study in carbon nanotube growth
Open accessAbstract
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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Cites
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Self-citationOpportunities and Challenges for Machine Learning in Materials Science
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Metadata
- Authors
- Nikolaev, P., Hooper, D., Webber, F., Rao, R., Decker, K., Krein, M., Poleski, J., Barto, R., & Maruyama, B.
- Venue
- npj Computational Materials
- Published
- 2016-10-21
- Volume
- 2
- Issue
- 1
- ISSN
- 2057-3960 (ISSN-L)
- OpenAlex
- W2558921396
- Cited by
- 379
- References
- 3
- FWCI
- 8.29
- Type
- journal article
- Language
- en
- Open access
- gold
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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