Persistent‐Homology‐Based Microstructural Optimization of Materials Using t‐Distributed Stochastic Neighbor Embedding. Issue 7 (5th June 2020)

• Record Type: Journal Article
• Authors: Wang, Zhi‐Lei; Ogawa, Toshio; Adachi, Yoshitaka
• Is Part Of: Advanced theory and simulations. Volume 3:Issue 7(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-06-05
• Subjects: machine learning -- microstructure optimization -- persistent homology -- similarity analysis -- t‐SNE; Science -- Simulation methods -- Periodicals; Science -- Methodology -- Periodicals; Engineering -- Simulation methods -- Periodicals; Engineering -- Methodology -- Periodicals; 507.21
• DOI: 10.1002/adts.202000040
• Languages: English
• ISSNs: 2513-0390

Abstract

Abstract: Microstructure optimization is a core issue to maximize the performance of materials. Due to the increasing demand for highly efficient materials, traditional trial‐and‐error‐based experimental methods have become insufficient for designing novel materials with useful properties. Based on the fact that materials with similar microstructural features exhibit similar properties, this work proposes a persistent‐homology‐based microstructure optimization approach performed with a machine learning algorithm of t‐distributed stochastic neighbor embedding to find optimal microstructures for specific properties. The method is applied to dual‐phase steels, where a microstructure with high‐fraction martensite is identified for achieving a maximum stress. The method proposed here is expected to provide new basis to understand the materials paradigm and thus accelerate the materials discovery process. Abstract : Materials with similar microstructural features generally exhibit similar properties. A persistent‐homology‐based microstructure optimization approach for finding optimal microstructures for specific properties is proposed in this work. The microstructural features corresponding to the desired properties are inversely explored using a genetic algorithm. The similarity of microstructures is identified using a t‐distributed stochastic neighbor embedding algorithm.