|About this Abstract
||7th World Congress on Integrated Computational Materials Engineering (ICME 2023)
||Microstructure-sensitive Materials Design via Efficient Uncertainty Propagation and Process-structure-property Linkages
||Vahid Attari, Danial Khatamsaz, Allison Kaye Ituralde Arabelo, Douglas Allaire, Raymundo Arroyave
|On-Site Speaker (Planned)
The field of Integrated Computational Materials Engineering (ICME) combines a broad range of methods to study materials’ responses over a spectrum of length scales. A relatively unexplored aspect of microstructure-sensitive materials design is uncertainty propagation and quantification (UP/UQ) of materials' microstructure, as well as establishing process-structure-property (PSP) relationships. An accurate UP technique built on the idea of changing probability measures for microstructure-based problems is proposed. Probability measures are used to represent microstructure space, and Wasserstein metrics are used to test the efficiency of the method. By using Variational AutoEncoder (VAE), we identify the correlations between the material/process parameters and the thermal conductivity of heterogeneous microstructures. Through high-throughput screening, UQ/UP, and deep-generative learning methods, PSP relationships that are too complicated/complex can be revealed through high-throughput exploitation of the materials' design space with an emphasis on microstructures.