Inferring topological transitions in pattern-forming processes with self-supervised learning

Journal Article · 22 September 2022 · npj Computational Materials

DOI: https://doi.org/10.1038/s41524-022-00889-2 · OSTI ID:1888859

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

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 physical vapor deposition of thin films. This approach opens a path forward for discovering unseen or hard-to-discern transitions and ultimately controlling complex pattern-forming processes.

• Sponsoring Organization: USDOE
• Grant/Contract Number: NONE; NA0003525
• OSTI ID: 1888859
• Journal Information: npj Computational Materials, Journal Name: npj Computational Materials Journal Issue: 1 Vol. 8; ISSN 2057-3960
• Publisher: Nature Publishing GroupCopyright Statement
• Country of Publication: United Kingdom
• Language: English