Descriptor-based reconstruction of three-dimensional microstructures through gradient-based optimization

TL;DR

This paper presents a flexible, gradient-based algorithm for reconstructing 3D microstructures from 2D images using differentiable descriptors, enabling insights into microstructure-property relationships.

Contribution

It introduces a novel, efficient optimization framework that allows flexible descriptor choices and handles noise in 2D micrograph-based microstructure reconstruction.

Findings

Demonstrates successful 3D microstructure reconstruction from 2D slices

Shows improved noise handling in the reconstruction process

Validates the method's efficiency through numerical experiments

Abstract

Microstructure reconstruction is an important cornerstone to the inverse materials design concept. In this work, a general algorithm is developed to reconstruct a three-dimensional microstructure from given descriptors. Based on two-dimensional (2D) micrographs, this reconstruction algorithm allows valuable insight through spatial visualization of the microstructure and in silico studies of structure-property linkages. The formulation ensures computational efficiency by casting microstructure reconstruction as a gradient-based optimization problem. Herein, the descriptors can be chosen freely, such as spatial correlations or Gram matrices, as long as they are differentiable with respect to the microstructure. Because real microstructure samples are commonly available as 2D microscopy images only, the desired descriptors for the reconstruction process are prescribed on orthogonal 2D slices. This adds a source of noise, which is handled in a new, superior and interpretable manner. The efficiency and applicability of this formulation is demonstrated by various numerical experiments.