  In this paper, we develop a convolutional neural network model to predict the
mechanical properties of a two-dimensional checkerboard composite
quantitatively. The checkerboard composite possesses two phases, one phase is
soft and ductile while the other is stiff and brittle. The ground-truth data
used in the training process are obtained from finite element analyses under
the assumption of plane stress. Monte Carlo simulations and central limit
theorem are used to find the size of the dataset needed. Once the training
process is completed, the developed model is validated using data unseen during
training. The developed neural network model captures the stiffness, strength,
and toughness of checkerboard composites with high accuracy. Also, we integrate
the developed model with a genetic algorithm (GA) optimizer to identify the
optimal microstructural designs. The genetic algorithm optimizer adopted here
has several operators, selection, crossover, mutation, and elitism. The
optimizer converges to configurations with highly enhanced properties. For the
case of the modulus and starting from randomly-initialized generation, the GA
optimizer converges to the global maximum which involves no soft elements.
Also, the GA optimizers, when used to maximize strength and toughness, tend
towards having soft elements in the region next to the crack tip.
