From smart homes to smart laboratories: Connected instruments for materials science

Mathieu Doucet

doi:10.1007/978-3-030-63393-6_17

From smart homes to smart laboratories: Connected instruments for materials science

Mathieu Doucet

Reflectometry

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

The current focus on artificial intelligence and machine learning in the scientific community has the potential to greatly speed up discovery. In this article, we explore what a “smart facility” would mean for materials science. We propose to capture meta-data at every step of an experiment, including materials synthesis, sample production and characterization, simulation, and the analysis software used to extract information. Although most of this information is captured in various institutional systems and staff logbooks, more insight could be obtained by connecting this information through a system that allows automation. AI-enabled processes built on such a system would have the potential of making experiment planning easier and minimize the time between experiment and publication.

Original language	English
Title of host publication	Driving Scientific and Engineering Discoveries Through the Convergence of HPC, Big Data and AI - 17th Smoky Mountains Computational Sciences and Engineering Conference, SMC 2020, Revised Selected Papers
Editors	Jeffrey Nichols, Arthur ‘Barney’ Maccabe, Suzanne Parete-Koon, Becky Verastegui, Oscar Hernandez, Theresa Ahearn
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	257-268
Number of pages	12
ISBN (Print)	9783030633929
DOIs	https://doi.org/10.1007/978-3-030-63393-6_17
State	Published - 2021
Event	17th Smoky Mountains Computational Sciences and Engineering Conference, SMC 2020 - Virtual, Online Duration: Aug 26 2020 → Aug 28 2020

Publication series

Name	Communications in Computer and Information Science
Volume	1315 CCIS
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	17th Smoky Mountains Computational Sciences and Engineering Conference, SMC 2020
City	Virtual, Online
Period	08/26/20 → 08/28/20

Funding

Acknowledgements. A portion of this research used resources at the SNS, a Department of Energy (DOE) Office of Science User Facility operated by ORNL. ORNL is managed by UT-Battelle LLC for DOE under Contract DE-AC05-00OR22725. The picture painted in this paper is the result of years worth of discussions with scientists in the fields of chemistry, physics, and computer science. In particular, I would like to thank Sudharshan Vazhkudai for discussions on data infrastructure and FAIR data, Rama Vasudevan for discussions on cross-facility data analytics, Jay Billings for discussions on machine learning, Dale Stansberry for discussions on data provenance, and Gabriel Veith for discussions on applying this approach to chemistry laboratories. I would like to thank John Hetrick and Jim Browning for discussing this manuscript and the overall vision. M. Doucet—Contributed Equally. This manuscript has been co-authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy. gov/downloads/doe-public-access-plan). A portion of this research used resources at the SNS, a Department of Energy (DOE)Office of Science User Facility operated by ORNL. ORNL is managed by UT-Battelle LLC for DOE under Contract DE-AC05-00OR22725. The picture painted in this paper is the result of years worth of discussions with scientists in the fields of chemistry, physics, and computer science. In particular, I would like to thank Sudharshan Vazhkudai for discussions on data infrastructure and FAIR data, Rama Vasudevan for discussions on cross-facility data analytics, Jay Billings for discussions on machine learning, Dale Stansberry for discussions on data provenance, and Gabriel Veith for discussions on applying this approach to chemistry laboratories. I would like to thank John Hetrick and Jim Browning for discussing this manuscript and the overall vision.

Keywords

Artificial intelligence
Connected instruments
FAIR data
Machine learning
Smart facilities

Access to Document

10.1007/978-3-030-63393-6_17

Cite this

Doucet, M. (2021). From smart homes to smart laboratories: Connected instruments for materials science. In J. Nichols, A. B. Maccabe, S. Parete-Koon, B. Verastegui, O. Hernandez, & T. Ahearn (Eds.), Driving Scientific and Engineering Discoveries Through the Convergence of HPC, Big Data and AI - 17th Smoky Mountains Computational Sciences and Engineering Conference, SMC 2020, Revised Selected Papers (pp. 257-268). (Communications in Computer and Information Science; Vol. 1315 CCIS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-63393-6_17

Doucet, Mathieu. / From smart homes to smart laboratories : Connected instruments for materials science. Driving Scientific and Engineering Discoveries Through the Convergence of HPC, Big Data and AI - 17th Smoky Mountains Computational Sciences and Engineering Conference, SMC 2020, Revised Selected Papers. editor / Jeffrey Nichols ; Arthur ‘Barney’ Maccabe ; Suzanne Parete-Koon ; Becky Verastegui ; Oscar Hernandez ; Theresa Ahearn. Springer Science and Business Media Deutschland GmbH, 2021. pp. 257-268 (Communications in Computer and Information Science).

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Doucet, M 2021, From smart homes to smart laboratories: Connected instruments for materials science. in J Nichols, AB Maccabe, S Parete-Koon, B Verastegui, O Hernandez & T Ahearn (eds), Driving Scientific and Engineering Discoveries Through the Convergence of HPC, Big Data and AI - 17th Smoky Mountains Computational Sciences and Engineering Conference, SMC 2020, Revised Selected Papers. Communications in Computer and Information Science, vol. 1315 CCIS, Springer Science and Business Media Deutschland GmbH, pp. 257-268, 17th Smoky Mountains Computational Sciences and Engineering Conference, SMC 2020, Virtual, Online, 08/26/20. https://doi.org/10.1007/978-3-030-63393-6_17

From smart homes to smart laboratories: Connected instruments for materials science. / Doucet, Mathieu.
Driving Scientific and Engineering Discoveries Through the Convergence of HPC, Big Data and AI - 17th Smoky Mountains Computational Sciences and Engineering Conference, SMC 2020, Revised Selected Papers. ed. / Jeffrey Nichols; Arthur ‘Barney’ Maccabe; Suzanne Parete-Koon; Becky Verastegui; Oscar Hernandez; Theresa Ahearn. Springer Science and Business Media Deutschland GmbH, 2021. p. 257-268 (Communications in Computer and Information Science; Vol. 1315 CCIS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Doucet M. From smart homes to smart laboratories: Connected instruments for materials science. In Nichols J, Maccabe AB, Parete-Koon S, Verastegui B, Hernandez O, Ahearn T, editors, Driving Scientific and Engineering Discoveries Through the Convergence of HPC, Big Data and AI - 17th Smoky Mountains Computational Sciences and Engineering Conference, SMC 2020, Revised Selected Papers. Springer Science and Business Media Deutschland GmbH. 2021. p. 257-268. (Communications in Computer and Information Science). doi: 10.1007/978-3-030-63393-6_17

From smart homes to smart laboratories: Connected instruments for materials science

Abstract

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Keywords

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