This event is part of Data-Driven Materials Informatics View Details

Data Sciences for Mesoscale and Macroscale Materials Models


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Contemporary computational materials science relies on an ecosystem of models that span an extremely broad range of characteristic time and length scales. These range from quantum mechanics-based methods at the smallest length and timescales to macroscale finite element approaches at the largest length scales. This includes for instance models to predict the evolution of defects in materials, such the kinetic Monte Carlo method or cluster dynamics; or models of plasticity that employ either dislocation dynamics at the mesoscopic scale or crystal plasticity, at the macroscopic scale. The evolution of defects and microstructures can in parallel be studied with experimental characterizations and imaging devices, e.g., techniques dedicated to monitoring the evolution of microstructures (such as grain coarsening with X-ray tomography).

A longstanding challenge in the field is to develop systematic techniques to leverage all available data sources to develop accurate materials models. However, due to the wide range of different computational model formulations and scales (phase field, discrete defect models, reaction-diffusion equations), of numerical approaches (spectral methods, finite elements, particle solvers), and of experimental data streams, mathematical challenges related to the design and efficient execution of data-driven meso and macro-scale models abound.

This workshop will focus on the challenge of informing meso and macro-scale models from data, either obtained from lower-scale computations or directly from experiments. Topics of interest include the use of data-driven methods to learn effective models from measured data (e.g., using sparse system identification methods, or backpropagation through PDE solvers), the development of rigorous data-driven scale-bridging techniques, or the development of optimal design of experiments methods to identify small sets of experiments or calculations that would best constrain the models at the lowest cost. We also welcome contributions related to high-throughput data generation approaches applicable to meso and macro-scale materials modeling and uncertainty quantification methods for data-driven models.


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Xingjie Li University of North Carolina at Charlotte
Marina Meila University of Washington
Danny Perez Los Alamos National Laboratory
Peter Voorhees Northwestern University


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