Deep-learning (DL) methods, in consideration of their excellence in dealing with highly complex structure-performance relationships for materials, are expected to become a new design paradigm for breakthroughs in material performance. However, in most cases, it is impractical to collect massive-scale experimental data or open-source theoretical databases to support training DL models with sufficient prediction accuracy. In a dataset consisting of 483 porous silicone rubber observations generated via ink-writing additive manufacturing, this work demonstrates that constructing low-dimensional, accurate descriptors is the prerequisite for obtaining high-precision DL models based on small experimental datasets. On this basis, a unique convolutional bidirectional long short-term memory model with spatiotemporal features extraction capability is designed, whose hierarchical learning mechanism further reduces the requirement for the amount of data by taking full advantage of data information. The proposed approach can be expected as a powerful tool for innovative material design on small experimental datasets, which can also be used to explore the evolutionary mechanisms of the structures and properties of materials under complex working conditions.
Keywords: additive manufacturing; deep learning; machine learning; material design; porous silicone rubber.
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