Descriptors play a central role in constructing composition–structure–property relationships to guide materials design. We propose a material descriptor, δτ, for the composition dependence of the Curie temperature (T_c) on single doping elements in BaTiO₃ ferroelectrics, which is then generalized to a linear combination of multiple dopants in the solid solutions. The descriptor δτ depends linearly on the Curie temperature and also serves to separate the ferroelectric phase from the relaxor phase. We compare δτ to other commonly used descriptors such as the tolerance factor, electronegativity, and ionic displacement. By using regression analysis on our assembled experimental data, we show how it outperforms other descriptors. We use the trained machine-learned models to predict compositions in our search space with the largest ferroelectric, dielectric, and piezoelectric properties, namely, d₃₃, electrostrain, and recoverable energy storage density. We experimentally verify our predictions for T_c and classification into ferroelectrics and relaxors by synthesizing and characterizing six solid solutions in BaTiO₃ ferroelectrics. Our definition of δτ can shed light on the design of knowledge-based descriptors in other systems such as Pb-based and Bi-based solid solutions.

中文翻译：

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基于BaTiO3的铁电体中相变的成分依赖性的基于知识的描述符。

描述符在构建组成－结构－属性关系以指导材料设计方面起着核心作用。我们提出了一种材料描述符δτ，以说明居里温度（T _c）对BaTiO _3中单个掺杂元素的成分依赖性。然后将其泛化为固溶体中多种掺杂剂的线性组合。描述子δτ线性地取决于居里温度，并且还用于将铁电相与弛豫相分离。我们将δτ与其他常用的描述符进行比较，例如容差因子，电负性和离子位移。通过对我们组装的实验数据进行回归分析，我们展示了它如何胜过其他描述符。我们使用训练有素的机器学习模型来预测搜索空间中具有最大铁电，介电和压电特性（即d ₃₃，电应变和可恢复的能量存储密度）的成分。我们通过实验验证了对T _c的预测通过合成和表征BaTiO ₃铁电体中的六种固溶体，将其分为铁电体和弛豫体。我们对δτ的定义可以阐明其他系统中基于知识的描述符的设计，例如基于Pb和Bi的固溶体。