Abstract

The identification of transitions in pattern-forming processes are critical to understand and fabricate microstructurally precise materials in many application domains. While supervised methods can be useful to identify transition regimes, they need labels, which require prior knowledge of order parameters or relevant microstructures describing these transitions. Instead, we develop a self-supervised, neural-network-based approach that does not require predefined labels about microstructure classes to predict process parameters from observed microstructures. We show that assessing the difficulty of solving this inverse problem can be used to uncover microstructural transitions. We demonstrate our approach by automatically discovering microstructural transitions in two distinct pattern-forming processes: the spinodal decomposition of a two-phase mixture and the formation of binary-alloy microstructures during …

Date

September 22, 2022

Authors

Marcin Abram, Keith Burghardt, Greg Ver Steeg, Aram Galstyan, Remi Dingreville

Journal

npj Computational Materials

Volume

8

Issue

1

Pages

205

Publisher

Nature Publishing Group UK