A data ecosystem to support machine learning in materials science


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.

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MDF: This work was performed under financial assistance award 70NANB14H012 from U.S. Department of Commerce, National Institute of Standards and Technology as part of the Center for Hierarchical Material Design (CHiMaD). This work was performed under the following financial assistance award 70NANB19H005 from U.S. Department of Commerce, National Institute of Standards and Technology as part of the Center for Hierarchical Materials Design (CHiMaD). This work was also supported by the National Science Foundation as part of the Midwest Big Data Hub under NSF Award Number: 1636950 “BD Spokes: SPOKE: MIDWEST: Collaborative: Integrative Materials Design (IMaD): Leverage, Innovate, and Disseminate.” DLHub: This work was supported in part by Laboratory Directed Research and Development funding from Argonne National Laboratory under U.S. Department of Energy under Contract DE-AC02-06CH11357. We also thank the Argonne Leadership Computing Facility for access to the PetrelKube Kubernetes cluster and Amazon Web Services for providing research credits to enable rapid service prototyping. This research used resources of the Argonne Leadership Computing Facility, a DOE Office of Science User Facility supported under Contract DE-AC02-06CH11357. The authors would also like to acknowledge and thank the researchers who made their datasets and/or models and codes openly available.[26,30,32]

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Blaiszik, B., Ward, L., Schwarting, M. et al. A data ecosystem to support machine learning in materials science. MRS Communications 9, 1125–1133 (2019). https://doi.org/10.1557/mrc.2019.118

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