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

作　　者：Marcin Abram Keith Burghardt Greg Ver Steeg Aram Galstyan Remi Dingreville 

机构地区：[1]Department of Physics and Astronomy,University of Southern California,Los Angeles,CA,90089,USA [2]Information Sciences Institute,University of Southern California,Marina del Rey,CA,90292,USA [3]Center for Integrated Nanotechnologies,Nanostructure Physics Department,Sandia National Laboratories,Albuquerque,NM,87185,USA

出　　处：《npj Computational Materials》2022年第1期1970-1981,共12页计算材料学（英文）

基　　金：RD acknowledges funding under the BeyondFingerprinting Sandia Grand Challenge Laboratory Directed Research and Development(GC LDRD)program;Department of Energy National Nuclear Security Administration under contract DE-NA0003525.

摘　　要：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.

关 键 词：ALLOY MICROSTRUCTURE TRANSITIONS 

分 类 号：O189[理学—数学]