|About this Abstract
||2020 TMS Annual Meeting & Exhibition
||Algorithm Development in Materials Science and Engineering
||Persistent homology: Unveiling the topological features in materials data
||Chaitali Patil, Lucas Magee, Supriyo Chakraborty, Yusu Wang, Stephen Niezgoda
|On-Site Speaker (Planned)
Persistent homology and persistence diagrams (PD) are important tools in a topological data analysis of large data sets to help effectively represent and analyze the underlying structures and features behind them. Such analysis is also useful to find the good descriptors for the machine learning. PDs are now being applied in many different research areas ranging from finance to biology. Specifically for materials science, new approaches had been proposed to classify and analyze defects, porosities, atomic structures and microstructures by using the PDs. Here, we present a case study for analyzing deformation behavior of the highly anisotropic material such as zirconium using the PDs. Zirconium was deformed under uniaxial compression at the room temperature using a 3D fast Fourier transform-based elasto-viscoplastic (FFT-EVP) crystal plasticity model. Effect of different textures on the evolution of the micromechanical fields was studied by means of PDs.
||Planned: Supplemental Proceedings volume