We show the ability to map the phase diagram of a relaxor-ferroelectric system as a function of temperature and composition through local hysteresis curve acquisition, with the voltage spectroscopy data being used as a proxy for the (unknown) microscopic state or thermodynamic parameters of materials. Given the discrete nature of the measurement points, we use Gaussian processes to reconstruct hysteresis loops in temperature and voltage space, and compare the results with the raw data and bulk dielectric spectroscopy measurements. The results indicate that the surface transition temperature is similar for all but one composition with respect to the bulk. Through clustering algorithms, we recreate the main features of the bulk diagram, and provide statistical confidence estimates for the reconstructed phase transition temperatures. We validate the method by using Gaussian processes to predict hysteresis loops for a given temperature for a composition unseen by the algorithm, and compare with measurements. These techniques can be used to map phase diagrams from functional materials in an automated fashion, and provide a method for uncertainty quantification and model selection.

我们展示了通过局部磁滞曲线采集来绘制弛豫铁电系统的相图作为温度和成分的函数的能力，其中电压光谱数据被用作材料的（未知）微观状态或热力学参数的代理。考虑到测量点的离散性，我们使用高斯过程来重构温度和电压空间中的磁滞回线，并将结果与​​原始数据和体电介质光谱测量结果进行比较。结果表明，相对于整体而言，除一种成分外，所有成分的表面转变温度均相似。通过聚类算法，我们重新创建了体图的主要特征，并为重建的相变温度提供了统计置信度估计。我们通过使用高斯过程预测该算法看不见的给定温度下的磁滞回线来验证该方法，并与测量值进行比较。这些技术可用于以自动化方式从功能材料映射相图，并提供不确定性量化和模型选择的方法。