AF: Small: Parallel Methods for Large, Atomic-scale Quantitative Analysis of Materials

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Parallel Methods for Large, Atomic-scale Quantitative Analysis of MaterialsProject SummaryExperimental advances in materials science have enabled near atomic scale imaging of materials, making it possible to relate atomic arrangements to macroscopic material properties and enabling smart materials design. Driven by the needs of the materials science community facing a rapid adoption of this new technology, the goal of the proposed research is to develop comprehensive algorithmic foundations for quantitative analysis of atomic-scale data from interpreting atom probe tomography images to their analysis and feature extraction. Parallel algorithms and high performance implementations will be emphasized due to the large data set sizes which can reach several hundred million to a billion atoms and beyond. The investigators will develop parallel algorithms to 1) deal with inherent limitations and errors in atomic sampling, 2) determine crystallographic orientation, 3) perform autocorrelation based clustering analysis, and 4) extract coherent structures and intrinsic geometric features through linear and non-linear manifold learning. The research will be carried out by an interdisciplinary team of PIs with expertise in parallel algorithms, high performance computing, computational and differential geometry, and materials science. The research is intended to advance materials science from visualization and simulation of atomistic scale solid state phenomena to its direct observation, quantitative analysis and interpretation.