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Characterizing microstructure-material-property relations calls for softwaretools which extract point-cloud- and continuum-scale-based representations ofmicrostructural objects. Application examples include atom probe, electron, andcomputational microscopy experiments. Mapping between atomic- andcontinuum-scale representations of microstructural objects results often inrepresentations which are sensitive to parameterization; however assessing thissensitivity is a tedious task in practice.  Here, we show how combining methods from computational geometry, collisionanalyses, and graph analytics yield software tools for automated analyses ofpoint cloud data for reconstruction of three-dimensional objects,characterization of composition profiles, and extraction of multi-parametercorrelations via evaluating graph-based relations between sets of meshedobjects. Implemented for point clouds with mark data, we discuss use cases inatom probe microscopy that focus on interfaces, precipitates, andcoprecipitation phenomena observed in different alloys. The methods areexpandable for spatio-temporal analyses of grain fragmentation, crystal growth,or precipitation.

On Strong-Scaling and Open-Source Tools for High-Throughput  Quantification of Material Point Cloud Data: Composition Gradients,  Microstructural Object Reconstruction, and Spatial Correlations