An eXtreme Gradient Boosting (XGBoost) machine learning model is built to predict the electrocaloric (EC) temperature change of a ceramic based on its composition (encoded by Magpie elemental properties), dielectric constant, Curie temperature, and characterization conditions. A dataset of 97 EC ceramics is assembled from the experimental literature. By sampling data from clusters in the feature space, the model can achieve a coefficient of determination of 0.77 and a root mean square error of 0.38 K for the test data. Feature analysis shows that the model captures known physics for effective EC materials. The Magpie features help the model to distinguish between materials, with the elemental electronegativities and ionic charges identified as key features. The model is applied to 66 ferroelectrics whose EC performance has not been characterized. Lead-free candidates with a predicted EC temperature change above 2 K at room temperature and 100 kV/cm are identified.

建立了一个 eXtreme Gradient Boosting (XGBoost) 机器学习模型，用于根据陶瓷的成分（由 Magpie 元素特性编码）、介电常数、居里温度和表征条件来预测陶瓷的电热 (EC) 温度变化。从实验文献中组装了 97 个 EC 陶瓷的数据集。通过从特征空间中的簇中采样数据，该模型可以实现测试数据的决定系数为 0.77 和均方根误差为 0.38 K。特征分析表明，该模型捕获了有效 EC 材料的已知物理特性。Magpie 特征有助于模型区分材料，元素电负性和离子电荷被确定为关键特征。该模型应用于 66 种 EC 性能尚未表征的铁电体。