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A data ecosystem to support machine learning in materials science

Published online by Cambridge University Press:  10 October 2019

Ben Blaiszik Open the ORCID record for Ben Blaiszik [Opens in a new window] ,
Logan Ward Open the ORCID record for Logan Ward [Opens in a new window] ,
Marcus Schwarting ,
Jonathon Gaff ,
Ryan Chard ,
Daniel Pike ,
Kyle Chard  and
Ian Foster Open the ORCID record for Ian Foster [Opens in a new window]
Ben Blaiszik*
Affiliation:
Globus, Department of Computer Science, University of Chicago, Chicago, IL, USA Data Science and Learning Division, Argonne National Laboratory, Lemont, IL, USA
Logan Ward
Affiliation:
Globus, Department of Computer Science, University of Chicago, Chicago, IL, USA Data Science and Learning Division, Argonne National Laboratory, Lemont, IL, USA
Marcus Schwarting
Affiliation:
Data Science and Learning Division, Argonne National Laboratory, Lemont, IL, USA
Jonathon Gaff
Affiliation:
Globus, Department of Computer Science, University of Chicago, Chicago, IL, USA
Ryan Chard
Affiliation:
Globus, Department of Computer Science, University of Chicago, Chicago, IL, USA Data Science and Learning Division, Argonne National Laboratory, Lemont, IL, USA
Daniel Pike
Affiliation:
Department of Computer Science, Cornell University, Ithaca, NY, USA
Kyle Chard
Affiliation:
Globus, Department of Computer Science, University of Chicago, Chicago, IL, USA Data Science and Learning Division, Argonne National Laboratory, Lemont, IL, USA
Ian Foster
Affiliation:
Globus, Department of Computer Science, University of Chicago, Chicago, IL, USA Data Science and Learning Division, Argonne National Laboratory, Lemont, IL, USA
*
Address all correspondence to Ben Blaiszik at [email protected]
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Abstract

Facilitating the application of machine learning (ML) 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 materials-specific ML 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 ML models and how users can access those capabilities through web and programmatic interfaces.

Type
Artificial Intelligence Research Letters
Information
MRS Communications , Volume 9 , Issue 4 , December 2019 , pp. 1125 - 1133
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Copyright
Copyright © Materials Research Society 2019

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