Machine-Learning Driven Predictive 3D Ramified Foam Fabrication and Mechanistic Understanding

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 towards electrocatalysis, flexible supercapacitors, and water disinfection. However, fabricating 3D superstructures with accurate structural characteristics remains exhaustive and challenging due to multiple 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 using a small training dataset. Our findings demonstrate the 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 into the synthetic mechanisms. Overall, this work represents a new advance in the ML-enabled predictive fabrication of complex structures and materials with mechanistic elucidation.