Research Area:  Machine Learning

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.

Keywords:  
microstructure optimization
trial-and-error
microstructural feature
persistent-homology
machine learning algorithm
dual-phase steels
materials paradigm

Author(s) Name:  Zhi-Lei Wang, Toshio Ogawa, Yoshitaka Adachi

Publisher name:  Wiley

DOI:  https://doi.org/10.1002/adts.202000040

Volume Information:  Volume 3

Paper Link: https://onlinelibrary.wiley.com/doi/abs/10.1002/adts.202000040