  The use of machine learning algorithms to investigate phase transitions in
physical systems is a valuable way to better understand the characteristics of
these systems. Neural networks have been used to extract information of phases
and phase transitions directly from many-body configurations. However, one
limitation of neural networks is that they require the definition of the model
architecture and parameters previous to their application, and such
determination is itself a difficult problem. In this paper, we investigate for
the first time the relationship between the accuracy of neural networks for
information of phases and the network configuration (that comprises the
architecture and hyperparameters). We formulate the phase analysis as a
regression task, address the question of generating data that reflects the
different states of the physical system, and evaluate the performance of neural
architecture search for this task. After obtaining the optimized architectures,
we further implement smart data processing and analytics by means of neuron
coverage metrics, assessing the capability of these metrics to estimate phase
transitions. Our results identify the neuron coverage metric as promising for
detecting phase transitions in physical systems.
