Transferable 3D convolutional neural networks for elastic constants prediction in nanoporous metals

The topology of nanoporous metals is crucial for determining their mechanical response. In this work, we generated 6,000 gold and 422 silver nanoporous structures and calculated three components of elastic modulus with Molecular Dynamics simulations, resulting in 19,263 data points. This study compared two distinct approaches of predicting elastic modulus: a Fully-Connected neural network trained on precomputed topological descriptors, and several 3D Convolutional neural network architectures adapted from computer vision. The 3D CNNs outperformed the descriptor-based baseline model ( ), with top-performing DenseNet-201 architecture achieving . Additionally, the effects of training grid resolution, dataset size, and descriptor integration into a model were investigated. We further demonstrated model robustness through Transfer learning: a pretrained model was fine-tuned on a much smaller dataset of denser gold structures and the dataset of denser silver structures. Finally, the trained model was employed to evaluate the mechanical properties of 100,000 stochastic nanoporous gold structures and identify the Pareto optimal designs. • 3D Convolutional neural nets directly learn nanoporous metals topology, surpassing descriptor baseline. • DenseNet-201 retains 92 % of score going from to training data resolution. • Training on 50 % data yields 4 % drop in score, demonstrating data-efficient learning. • Transfer learning adapts NPG-trained model to a new dataset with a few additional samples. • Screening 100,000 stochastic designs identifies Pareto-optimal nanoporous gold structures.