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The goal of much research in computational materials science is to quantify necessary morphological information and then to develop stochastic models which both accurately reflect the material morphology and allow one to estimate macroscopic physical properties. A novel method of characterizing the morphology of disordered systems is presented based on the evolution of a family of integral geometric measures during erosion and dilation operations. The method is used to determine the accuracy of model reconstructions of random systems. It is shown that the use of erosion/dilation operations on the original image leads to a more accurate discrimination of morphology than previous methods.
The goal of much research in computational materials science is to quantify necessary morphological information and then to develop stochastic models and then to develop stochastic models which both accurately reflect the material morphology and allow one to estimate macroscopic physical properties. A novel method of characterizing the morphology of disordered systems is presented based on the evolution of a family of integral geometric measures during erosion and dilation operations. The method is used to determine the accuracy of model reconstructions of random systems. It is shown that the use of erosion / dilation operations on the original image leads to a more accurate discrimination of morphology than previous methods.