Persistent‐Homology‐Based Microstructural Optimization of Materials Using t‐Distributed Stochastic Neighbor Embedding
Zhilei Wang, Toshio Ogawa, Yoshitaka Adachi · Advanced Theory and Simulations · 2020
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.