This dataset contains the training and test data, as well as the trained neural networks as used for the paper 'Machine Learning of Implicit Combinatorial Rules in Mechanical Metamaterials', as published in Physical Review Letters. In this paper, a neural network is used to classify each \(k \times k\) unit cell design of metamaterial M1 and M2 into one of two classes (C or I). Additionally, the performance of the trained networks is analysed in detail. A more detailed description of the contents of the dataset follows below. NeuralNetwork_train_and_test_data.zip This file contains the train and test data used to train the Convolutional Neural Networks (CNNs) of the paper. Each unit cell size has its own file, and is saved in a zipped numpy file type (.npz). It contains data for metamaterial M1 ("smiley_cube"), and metamaterial M2 classification (i) ("prek_xy") and (ii) ("unimodal_vs_oligomodal_inc_stripmodes"). CNN_saves_kxk.zip This file contains the parameter configurations of the CNNs trained on \(k \times k\) unit cells for metamaterial M2 classification (ii). Classification (i) is denoted by an additional M2ii in the file name. Metamaterial M1 is denoted by an extra M1 in the file name. Every hyperparameter (number of filters nf, number of hidden neurons nh, learning rate lr) combination is saved separately. The neural networks can be loaded using Google's TensorFlow package in Python, specifically using the 'tf.keras.models.load_model' function.

{"references": ["van Mastrigt, R., Dijkstra, M., van Hecke, M., & Coulais, C. (2022). Machine Learning of Implicit Combinatorial Rules in Mechanical Metamaterials. Physical Review Letters, 129(19), 198003."]}