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.

中文翻译：

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机器学习驱动的预测性 3D 分支泡沫制造和机理理解


分层纳米超结构在自然界中无处不在，由于其独特的 3D 级联特征，呈现双重增强的质量传递和界面化学反应。它们的人造对应物在电催化、柔性超级电容器和水消毒方面显示出卓越的优势。然而，由于实验条件和结构特征的众多变量，制造具有精确结构特性的 3D 超结构仍然很详尽且具有挑战性。在这项工作中，我们探索了三种机器学习 （ML） 方法——线性回归、神经网络回归和高斯过程回归——并首次通过使用小型训练数据集实现了设计的 3D 微分支泡沫的准确预测制造。我们的研究结果表明，高斯过程回归在所有基准中的准确性都超过 87%。我们还有效地揭示了各种实验条件的加权作用，阐明了合成机制。总体而言，这项工作代表了通过机理阐明对复杂结构和材料进行 ML 预测制造的新进展。