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Progress and prospects for accelerating materials science with automated and autonomous workflows

Stein, Helge S. and Gregoire, John M. (2019) Progress and prospects for accelerating materials science with automated and autonomous workflows. Chemical Science, 10 (42). pp. 9640-9649. ISSN 2041-6520. PMCID PMC7020936. doi:10.1039/c9sc03766g.

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Accelerating materials research by integrating automation with artificial intelligence is increasingly recognized as a grand scientific challenge to discover and develop materials for emerging and future technologies. While the solid state materials science community has demonstrated a broad range of high throughput methods and effectively leveraged computational techniques to accelerate individual research tasks, revolutionary acceleration of materials discovery has yet to be fully realized. This perspective review presents a framework and ontology to outline a materials experiment lifecycle and visualize materials discovery workflows, providing a context for mapping the realized levels of automation and the next generation of autonomous loops in terms of scientific and automation complexity. Expanding autonomous loops to encompass larger portions of complex workflows will require integration of a range of experimental techniques as well as automation of expert decisions, including subtle reasoning about data quality, responses to unexpected data, and model design. Recent demonstrations of workflows that integrate multiple techniques and include autonomous loops, combined with emerging advancements in artificial intelligence and high throughput experimentation, signal the imminence of a revolution in materials discovery.

Item Type:Article
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URLURL TypeDescription CentralArticle
Stein, Helge S.0000-0002-3461-0232
Gregoire, John M.0000-0002-2863-5265
Additional Information:© 2019 The Royal Society of Chemistry. This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. All publication charges for this article have been paid for by the Royal Society of Chemistry. The article was received on 26 Jul 2019, accepted on 19 Sep 2019 and first published on 20 Sep 2019. This material is based upon work supported by the Air Force Office of Scientific Research under award number FA9550-18-1-0136, the Office of Science of the U.S. Department of Energy under Award No. DE-SC0004993, and an Accelerated Materials Design and Discovery grant from the Toyota Research Institute.
Funding AgencyGrant Number
Air Force Office of Scientific Research (AFOSR)FA9550-18-1-0136
Department of Energy (DOE)DE-SC0004993
Toyota Research InstituteUNSPECIFIED
Issue or Number:42
PubMed Central ID:PMC7020936
Record Number:CaltechAUTHORS:20191016-131623439
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:99304
Deposited By: Tony Diaz
Deposited On:16 Oct 2019 20:49
Last Modified:16 Feb 2022 00:01

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