The microstructure of a material,typically characterized through a set of microscopy images of two-dimensional cross-sections,is a valuable source of information about the material and its properties.Every pixel of th...The microstructure of a material,typically characterized through a set of microscopy images of two-dimensional cross-sections,is a valuable source of information about the material and its properties.Every pixel of the image is a degree of freedom causing the dimensionality of the information space to be extremely high.This makes it difficult to recognize and extract all relevant information from the images.Human experts circumvent this by manually creating a lower-dimensional representation of the microstructure.However,the question of how a microstructure image can be best represented remains open.From the field of deep learning,we present triplet networks as a method to build highly compact representations of the microstructure,condensing the relevant information into a much smaller number of dimensions.We demonstrate that these representations can be created even with a limited amount of example images,and that they are able to distinguish between visually very similar microstructures.We discuss the interpretability and generalization of the representations.Having compact microstructure representations,it becomes easier to establish processing–structure–property links that are key to rational materials design.展开更多
基金M.L.and S.C.acknowledge financial support from OCAS NV by an OCAS-sponsored PhD position and by an OCAS-endowed chair at Ghent University,respectivelyThe computational resources and services used in this work were provided by the VSC(Flemish Supercomputer Center),funded by the Research Foundation–Flanders(FWO)and the Flemish Government–department EWI.
文摘The microstructure of a material,typically characterized through a set of microscopy images of two-dimensional cross-sections,is a valuable source of information about the material and its properties.Every pixel of the image is a degree of freedom causing the dimensionality of the information space to be extremely high.This makes it difficult to recognize and extract all relevant information from the images.Human experts circumvent this by manually creating a lower-dimensional representation of the microstructure.However,the question of how a microstructure image can be best represented remains open.From the field of deep learning,we present triplet networks as a method to build highly compact representations of the microstructure,condensing the relevant information into a much smaller number of dimensions.We demonstrate that these representations can be created even with a limited amount of example images,and that they are able to distinguish between visually very similar microstructures.We discuss the interpretability and generalization of the representations.Having compact microstructure representations,it becomes easier to establish processing–structure–property links that are key to rational materials design.
