How to make overfitting (powerful) model?
Here are some general hyperparameter adjustments to increase model capacity:


Architecture-related:


add additional layers
increase the number of hidden units per layer
remove dropout or decrease dropout rates

Optimizer related:


find the optimal learning rate
train for more epochs



Section 11.4.1 in the Deep Learning Book provides a good overview. The paper "Practical recommendations for gradient-based training of deep architectures" is another good source to understand the effect of different hyperparameters (though it is a bit older). 

Moreover, there are specific hyperparameters to increase model capacity for different network types, e.g. 


kernel size for CNNs, 
sequence length for LSTMs and
embedding dimension for embedding layers.