Exploration of phase transitions and construction of associated phase diagrams are of fundamental importance for condensed matter physics and materials science alike, and remain the focus of extensive research for both theoretical and experimental studies. For the latter, comprehensive studies involving scattering, thermodynamics, and modeling are typically required. We present a new approach to data mining multiple realizations of collective dynamics, measured through piezoelectric relaxation studies, to identify the onset of a structural phase transition in nanometer-scale volumes, that is, the probed volume of an atomic force microscope tip. Machine learning is used to analyze the multidimensional data sets describing relaxation to voltage and thermal stimuli, producing the temperature-bias phase diagram for a relaxor crystal without the need to measure (or know) the order parameter. The suitability of the approach to determine the phase diagram is shown with simulations based on a two-dimensional Ising model. These results indicate that machine learning approaches can be used to determine phase transitions in ferroelectrics, providing a general, statistically significant, and robust approach toward determining the presence of critical regimes and phase boundaries.

中文翻译：

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基于实验数据的基于机器学习的材料相变识别：探索铁电弛豫器的集体动力学。

相变的探索和相关相图的构建对于凝聚态物理和材料科学都具有根本的重要性，并且仍然是理论研究和实验研究广泛研究的重点。对于后者，通常需要进行涉及散射，热力学和建模的全面研究。我们提出了一种新的方法，用于通过压电弛豫研究对集体动力学的多种实现进行数据挖掘，以识别纳米级体积（即原子力显微镜尖端的探测体积）中结构相变的开始。机器学习用于分析描述电压和热刺激松弛的多维数据集，在无需测量（或知道）阶数参数的情况下，生成弛豫器晶体的温度偏置相图。基于二维Ising模型的仿真显示了确定相图的方法的适用性。这些结果表明，机器学习方法可用于确定铁电体中的相变，从而为确定临界状态和相界的存在提供了一种通用的，具有统计意义的强大方法。