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.
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).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Buckberry, H., Burke, J., Starke, M., et al.: Smart technologies enable homes to be efficient and interactive with the grid ORNL/TM-2020/1507 (2020). https://doi.org/10.2172/1615193
Intelligent Transportation Systems Joint Program Office, Strategic Plan 2020–2025, FHWA-JPO-18-746 (2020). https://www.its.dot.gov/stratplan2020/
Ratner, D., Sumpter, B., Alexander, F., et al.: BES roundtable on producing and managing large scientific data with artificial intelligence and machine learning (2019). https://doi.org/10.2172/1630823
Sivia, D.S.: Elementary scattering theory for X-ray and neutron users. Oxford University Press (2011). https://doi.org/10.1093/acprof:oso/9780199228676.001.0001
Veith, G.M., Doucet, M., Baldwin, J.K., et al.: Direct determination of solid-electrolyte interphase thickness and composition as a function of state of charge on a silicon anode. J. Phys. Chem. C 119(35), 20339–20349 (2015). https://doi.org/10.1021/acs.jpcc.5b06817
Fears, T.M., Doucet, M., Browning, J.F., et al.: Evaluating the solid electrolyte interphase formed on silicon electrodes: a comparison of ex situ X-ray photoelectron spectroscopy and in situ neutron reflectometry. Phys. Chem. Chem. Phys. 18, 13927–13940 (2016). https://doi.org/10.1039/C6CP00978F
Veith, G.M., Doucet, M., Sacci, R.L., et al.: Determination of the Solid Electrolyte Interphase Structure Grown on a Silicon Electrode Using a Fluoroethylene Carbonate Additive. Sci. Rep. 7, 6326 (2017). https://doi.org/10.1038/s41598-017-06555-8
Browning, K.L., Browning, J.F., Doucet, M., et al.: Role of conductive binder to direct solid-electrolyte interphase formation over silicon anodes. Phys. Chem. Chem. Phys. 21(31), 17356–17365 (2019). https://doi.org/10.1039/C9CP02610J
Browning, K.L., Sacci, R.L., Doucet, M., et al.: The study of the binder poly(acrylic acid) and its role in concomitant solid-electrolyte interphase formation on Si anodes. ACS Appl. Mater. Interfaces 12(8), 10018–10030 (2020). https://doi.org/10.1021/acsami.9b22382
Wang, A., Kadam, S., Li, H. et al.: Review on modeling of the anode solid electrolyte interphase (SEI) for lithium-ion batteries NPJ Computational Materials 4:15 (2018). https://doi.org/10.1038/s41524-018-0064-0
Wilkinson, M., Dumontier, M., Aalbersberg, I., et al.: The FAIR Guiding Principles for scientific data management and stewardship. Sci. Data 3, 160018 (2016). https://doi.org/10.1038/sdata.2016.18
Elgrishi, N., Rountree, K.J., McCarthy, B.D., et al.: A practical beginner’s guide to cyclic voltammetry. J. Chem. Educ. 95, 197–206 (2018). https://doi.org/10.1021/acs.jchemed.7b00361
Könnecke, M., Akeroyd, F.A., Bernstein, H.J., et al.: The NeXus data format. J. Appl. Cryst. 48, 301–305 (2015). https://doi.org/10.1107/S1600576714027575
Shipman, G., Campbell, S., David Dillow, D., et al.: Accelerating data acquisition, reduction, and analysis at the spallation neutron source. In: IEEE 10th International Conference on eScience (2014). https://doi.org/10.1109/eScience.2014.31
Parker, P.G., Ren, S.: ONCat (ORNL Neutron Catalog) (2018). https://doi.org/10.11578/dc.20200513.5
Kilicoglu, H., Shin, D., Fiszman, M., Rosemblat, G., Rindflesch, T.C.: SemMedDB: a PubMed-scale repository of biomedical semantic predications. Bioinformatics 28(23), 3158–60 (2012). https://doi.org/10.1093/bioinformatics/bts591
Writer, B.: Lithium-ion batteries: a machine-generated summary of current research. Springer, Heidelberg (2019). https://doi.org/10.1007/978-3-030-16800-1
Jain, A., Ong, S.P., Hautier, G., et al.: Commentary: the materials project: a materials genome approach to accelerating materials innovation. APL Materials 1, 011002 (2013). https://doi.org/10.1063/1.4812323
Zhao, S., Qian, Q.: Ontology based heterogeneous materials database integration and semantic query. AIP Adv. 7, 105325 (2017). https://doi.org/10.1063/1.4999209
Stansberry, D., Somnath, S., Breet, J. DataFed: Towards Reproducible Research via Federated Data Management 2019 International Conference on Computational Science and Computational Intelligence (CSCI), pp. 1312-1317 (2019). https://doi.org/10.1109/CSCI49370.2019.00245
Götz, A., Bodera Sempere, J., Campbell, A. et al.: Enabling Open Science for Photon and Neutron Sources, Proceedings of ICALEPCS2019, PROCEEDING-2020-029, 2085 (2019)
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply
About this paper
Cite this paper
Doucet, M. (2020). From Smart Homes to Smart Laboratories: Connected Instruments for Materials Science. In: Nichols, J., Verastegui, B., Maccabe, A.‘., Hernandez, O., Parete-Koon, S., Ahearn, T. (eds) Driving Scientific and Engineering Discoveries Through the Convergence of HPC, Big Data and AI. SMC 2020. Communications in Computer and Information Science, vol 1315. Springer, Cham. https://doi.org/10.1007/978-3-030-63393-6_17
Download citation
DOI: https://doi.org/10.1007/978-3-030-63393-6_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-63392-9
Online ISBN: 978-3-030-63393-6
eBook Packages: Computer ScienceComputer Science (R0)