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.

翻译：利用机器学习算法研究物理系统中的相变是深入理解这些系统特性的重要途径。神经网络已被用于直接从多体构型中提取相与相变信息。然而，神经网络的局限性在于其应用前需要预先定义模型架构与参数，而这一确定过程本身即是一个难题。本文首次探究了神经网络用于相信息时的准确性与网络配置（包括架构与超参数）之间的关系。我们将相分析表述为回归任务，解决了如何生成反映物理系统不同状态的数据问题，并评估了神经架构搜索在该任务中的性能。在获得优化架构后，我们进一步通过神经元覆盖度指标实现智能数据处理与分析，评估了这些指标用于估计相变的能力。我们的研究结果表明，神经元覆盖度指标在检测物理系统相变方面具有良好前景。