| Abstract Scope |
Materials data science for reliability requires large amounts of data and data types, often from disparate datastreams. This requires a different approach to data collection, handling, and curation in order to build models including data-drive models, machine learning, and graph models. FAIRification of data and models is necessary to combine datasets efficiently through time. Ontologies are necessary to link datasets together in schemas to provide insights into the relationships between data in knowledge graphs. Defects in photovoltaic (PV) cells are identified in multiple different image types including fluorescence, electroluminescence, IR, and white light imaging which all identify different defect features and degradation signatures. These images must be combined into hyper images for better feature extraction of large sets of images which are then coupled with I-V curve and power data from these cells. |