A data ecosystem to support machine learning in materials science

Blaiszik, Ben; Ward, Logan; Schwarting, Marcus; Gaff, Jonathon; Chard, Ryan; Pike, Daniel; Chard, Kyle; Foster, Ian

doi:10.1557/mrc.2019.118

A data ecosystem to support machine learning in materials science

Journal Article · Thu Oct 10 04:00:00 EDT 2019 · MRS Communications

DOI:https://doi.org/10.1557/mrc.2019.118· OSTI ID:1607645

^[1]; ^[1]; Schwarting, Marcus ^[2]; Gaff, Jonathon ^[3]; Chard, Ryan ^[1]; Pike, Daniel ^[4]; Chard, Kyle ^[1]; ^[1]

Univ. of Chicago, IL (United States); Argonne National Lab. (ANL), Argonne, IL (United States)
Argonne National Lab. (ANL), Argonne, IL (United States)
Univ. of Chicago, IL (United States)
Cornell Univ., Ithaca, NY (United States)

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.

View Accepted Manuscript (DOE)

Research Organization:: Argonne National Laboratory (ANL), Argonne, IL (United States)

Sponsoring Organization:: USDOE Office of Science (SC); National Inst. of Standards and Technology (NIST), Boulder, CO (United States)

Grant/Contract Number:: AC02-06CH11357

OSTI ID:: 1607645

Journal Information:: MRS Communications, Journal Name: MRS Communications Journal Issue: 4 Vol. 9; ISSN 2159-6859

Publisher:: Materials Research Society - Cambridge University PressCopyright Statement

Country of Publication:: United States

Language:: English

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