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Title: A data ecosystem to support machine learning in materials science

Abstract

Facilitating the application of machine learning to materials science problems requires enhancing the data ecosystem to enable discovery and collection of data from many sources, automated dissemination of new data across the ecosystem, and the connecting of data with materialsspecific machine learning models. Here, we present two projects, the Materials Data Facility (MDF) and the Data and Learning Hub for Science (DLHub), that address these needs. We use examples to show how MDF and DLHub capabilities can be leveraged to link data with machine learning models and how users can access those capabilities through web and programmatic interfaces.

Authors:
ORCiD logo [1]; ORCiD logo [1];  [2];  [3];  [1];  [4];  [1]; ORCiD logo [1]
+ Show Author Affiliations
  1. Univ. of Chicago, IL (United States); Argonne National Lab. (ANL), Argonne, IL (United States)
  2. Argonne National Lab. (ANL), Argonne, IL (United States)
  3. Univ. of Chicago, IL (United States)
  4. Cornell Univ., Ithaca, NY (United States)
Publication Date:
Research Org.:
Argonne National Laboratory (ANL), Argonne, IL (United States)
Sponsoring Org.:
USDOE Office of Science (SC); National Inst. of Standards and Technology (NIST), Boulder, CO (United States)
OSTI Identifier:
1607645
Grant/Contract Number:  
AC02-06CH11357
Resource Type:
Accepted Manuscript
Journal Name:
MRS Communications
Additional Journal Information:
Journal Volume: 9; Journal Issue: 4; Journal ID: ISSN 2159-6859
Publisher:
Materials Research Society - Cambridge University Press
Country of Publication:
United States
Language:
English
Subject:
36 MATERIALS SCIENCE

Citation Formats

Blaiszik, Ben, Ward, Logan, Schwarting, Marcus, Gaff, Jonathon, Chard, Ryan, Pike, Daniel, Chard, Kyle, and Foster, Ian. A data ecosystem to support machine learning in materials science. United States: N. p., 2019. Web. doi:10.1557/mrc.2019.118.
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Blaiszik, Ben, Ward, Logan, Schwarting, Marcus, Gaff, Jonathon, Chard, Ryan, Pike, Daniel, Chard, Kyle, & Foster, Ian. A data ecosystem to support machine learning in materials science. United States. https://doi.org/10.1557/mrc.2019.118
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Blaiszik, Ben, Ward, Logan, Schwarting, Marcus, Gaff, Jonathon, Chard, Ryan, Pike, Daniel, Chard, Kyle, and Foster, Ian. Thu . "A data ecosystem to support machine learning in materials science". United States. https://doi.org/10.1557/mrc.2019.118. https://www.osti.gov/servlets/purl/1607645.
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@article{osti_1607645,
title = {A data ecosystem to support machine learning in materials science},
author = {Blaiszik, Ben and Ward, Logan and Schwarting, Marcus and Gaff, Jonathon and Chard, Ryan and Pike, Daniel and Chard, Kyle and Foster, Ian},
abstractNote = {Facilitating the application of machine learning to materials science problems requires enhancing the data ecosystem to enable discovery and collection of data from many sources, automated dissemination of new data across the ecosystem, and the connecting of data with materialsspecific machine learning models. Here, we present two projects, the Materials Data Facility (MDF) and the Data and Learning Hub for Science (DLHub), that address these needs. We use examples to show how MDF and DLHub capabilities can be leveraged to link data with machine learning models and how users can access those capabilities through web and programmatic interfaces.},
doi = {10.1557/mrc.2019.118},
journal = {MRS Communications},
number = 4,
volume = 9,
place = {United States},
year = {Thu Oct 10 00:00:00 EDT 2019},
month = {Thu Oct 10 00:00:00 EDT 2019}
}
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Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
https://doi.org/10.1557/mrc.2019.118
Copyright Statement
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Citation Metrics:
Cited by: 71 works
Citation information provided by
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Figures / Tables:

Figure 1 Figure 1: Materials Data Facility (MDF) overview. (1) Users submit data to MDF by specifying the data’s location, title, authors, and more. (2) MDF Connect collects data from the specified location and applies materials-specific extractors and transformations to enrich the data. (3) Processed data and metadata are dispatched to anymore » supported community data service(s) specified by the user. Other users can then discover, interact with, and access the data using any of those services.« less
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