Workshop on the Convergence of Materials Research and Multi-Sensory Data Science

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The creation of large data sets in materials research is transforming the way in which work is done in the materials research field, making data science one of the essential tools in the discovery of new materials and in the elucidation of structure/processing/property correlations. At present, many materials problems of technological relevance are inherently data-intensive. Lack of adequate frameworks to process complex raw data into sound scientific knowledge hinders progress in crucial fronts, such as the correlation of grain-boundary character distribution of materials microstructures, along with its many degrees of freedom, with thermo-mechanical properties; and the identification of mechanisms controlling both normal and abnormal grain growth to engineer bulk, nanograin materials, amongst others. Lack of available frameworks often results in acquired data being discarded, given the poor current understanding on how to process and analyze big data. In short, materials scientists are inundated with multi-dimensional data from many sources, both experimental and computational, and vast amounts of data are under-utilized or thrown out completely. The purpose of this workshop is to build the needed framework by assembling four seemingly disparate platforms: Human Computer Interactions, Data Analytics, Fundamental Research in Materials, and New Tools. This workshop proposes a genuine convergence approach merging two NSF Big Ideas, where the pursuit of harnessing data through improved human-machine interaction presents a new framework for materials discovery. Outputs to this workshop include a roadmap to guide the framework, as well as the nucleation of transdisciplinary collaborations amongst participants and several education opportunities, including recruitment, early-community building, and publicly available education materials suitable for university courses. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.