Large set microstructure reconstruction mimicking quantum computing approach via deep learning

Microstructural characterization and reconstruction (MCR) is critical for unearthing processing-structure property (PSP) links in new materials discovery, design and development. However, the inherent generation of large sets of digital microstructures remains challenging due to the optimization requirements of conventional MCR platforms. In this study, we designed a new framework of MCR mimicking quantum computing (QC) approach to boost the speed of reconstructions. A 2D probabilistic map was utilized as input which contains multi point correlation functions of the microstructure. The designed framework generates 3D microstructures from the probabilistic map based on a set of parameters calibrated via a deep learning algorithm. Such a framework converts the optimization process of MCR into a parameter extraction process replicating Shor's algorithm. The improved efficiency allows material scientists to build sensible PSP links via simulation and data-mining techniques. This method also demonstrates a potential methodology to achieve quantum supremacy with the aid of deep learning using a classical computer. (c) 2022 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

Automated summary

Microstructural characterization and reconstruction (MCR) is crucial for understanding the relationship between processing, structure, and properties in materials. This study proposes a framework that mimics quantum computing to enhance the speed of reconstructions, using a deep learning algorithm to calibrate parameters. This method enables material scientists to establish meaningful links between processing, structure, and properties through simulation and data-mining techniques.