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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                            Tapis Machine Learning Hub Service for Science Gateways
                        
                    
    
            The adaptation of machine learning (ML) in scientific and medical research in recent years has heralded a new era of innovation, catalyzing breakthroughs that were once deemed unattainable. In this paper, we present the Machine Learning Hub (ML Hub) – a web application offering a single point of access to pre-trained ML models and datasets, catering to users across varying expertise levels. Built upon the NSF-funded Tapis v3 Application Programming Interface (API) and Tapis User Interface (TapisUI), the platform offers a user-friendly interface for model discovery, dataset exploration, and inference server deployment. 
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                            - Award ID(s):
- 2231406
- PAR ID:
- 10628193
- Publisher / Repository:
- Zenodo
- Date Published:
- Subject(s) / Keyword(s):
- Machine Learning Tapis Open-Source Science Gateways
- Format(s):
- Medium: X
- Right(s):
- Creative Commons Attribution 4.0 International
- Sponsoring Org:
- National Science Foundation
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