Elements: Data-Science Methods for Resource Allocation During Characterization of Dynamic Systems

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The project develops the software infrastructure and its integration with physical infrastructure that is required to bring state-of-the-art data science, machine learning, and artificial Intelligence (AI) tools to novel materials science experiments at a national user facility. The work will create an openly available control package that will enable dynamic experiments informed by modeling in real-time. The developments will make more efficient use of scarce beam-time at national synchrotron user facilities, enabling higher scientific throughput for in-situ experiments probing the mechanical response of materials under load.The effort evaluates the hypothesis that rare material failure events can be predicted from a small number of features that describe evolving local material states using machine learning solutions. The project is focusing on synchrotron x-ray scattering measurements of materials under mechanical load, and specifically integrating new and existing toolsets into a control package capable of dynamic resource allocation for hyper-efficient data collection at the Cornell High Energy Synchrotron Source (CHESS), a national user facility. The project integrates these toolsets to detect precursor signatures through real-time processing of data from user facilities such as CHESS, and suggests resource allocations to facilitate study of early stages of stochastic events in dynamic materials systems. The goal is to develop machine learning (ML) techniques and software infrastructure to inform the best, in a probabilistic sense, allocation of limited detector resources at material testing facilities, to better capture early stages of rare events in materials and the key factors for these events. The effort is also interested in applying the same resource allocation strategies to computational resource allocation in simulations of materials systems. This award by the Office of Advanced Cyberinfrastructure is jointly supported by the Division of Civil, Mechanical and Manufacturing Innovation (CMMI) within the NSF Directorate for Engineering, and the Division of Materials Research (DMR) within the Directorate for Mathematical and Physical Sciences.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.