Y. Liu
The University of texas at austin,
United States
Keywords: machine learning, hierarchical structure, electrodeposition, gaussian process regression
Summary:
Hierarchical nanosuperstructures, ubiquitously found in nature, present dually enhanced mass transport and interfacial chemical reactions due to their unique 3D cascading features. Their man-made counterparts have demonstrated meritorious benefits toward electrocatalysis, flexible supercapacitors, and water disinfection. However, fabricating 3D superstructures with accurate structural characteristics remains exhaustive and challenging due to a multitude of variables in both experimental conditions and structural features. In this work, we explore three machine learning (ML) methods─linear regression, neural network regression, and Gaussian process regression─and, for the first time, realize accurate predictive fabrication of designed 3D microbranched foams by using a small training data set. Our findings demonstrate an advantageous accuracy of Gaussian process regression of over 87% across all benchmarks. We also effectively unravel the weighted roles of various experimental conditions, shedding light on the synthetic mechanisms. Overall, this work represents a new advance in the ML-enabled predictive fabrication of complex structures and materials with mechanistic elucidation.