Constructing Structure‐Property Maps using High‐Throughput Experiments and Machine Learning

ABSTRACT A fundamental challenge in materials science is to map structures to properties. Physics‐based models are limited by theoretical idealizations, whereas data‐driven models are limited by the size of available experimental datasets. Here, we demonstrate an approach to construct structure‐property maps through a combination of a high‐throughput experiment and machine learning. We print thousands of samples, the structure of each being defined by a pixel arrangement. We then measure the stress–strain curves of these samples by developing a high‐throughput experiment. From these, we derived stiffness, fracture strain, fracture stress, and work of fracture, forming a dataset linking pixelated structures to properties. A convolutional neural network, initialized on ImageNet and fine‐tuned by transfer learning, learned maps that generalize: out‐of‐sample test errors were 2.44%, 7.05%, 4.65%, and 12.16% for the four properties, despite the design space (∼10 35 ) far exceeding the fabricated set (∼10 3 ). Embedding the learned map within an active learning closed loop to optimize fracture stress and fracture strain. Within a few iterations, it discovered a bar‐like topology for fracture stress (94% improvement) and a zig‐zag topology for fracture strain (282% improvement). These results demonstrate that high‐throughput experiments combined with machine learning can construct reliable structure–property maps and effectively optimize fracture properties.