面向复合材料带隙预测的两段式集成学习模型构建

Two-stage ensemble learning model for predicting band gaps of composites

带隙是钙钛矿型复合氧化物材料重要的特征参数,对材料的物理化学性质起决定性作用,如导电性能和光电性能等.为了寻找适合不同应用领域的钙钛矿型材料,利用机器学习进行带隙预测是一种重要的研究手段.构建了一个两阶段异质集成学习模型,在第一阶段使用多种不同的基础机器学习器(回归模型)进行预测;在第二阶段把对预测结果影响较大的描述子和基础机器学习器进行集成学习.利用该模型对210种钙钛矿型复合氧化物材料的带隙进行预测,并与多种独立的机器学习算法以及不同集成策略模型的预测性能相对比,评估了本模型的预测性能.结果表明,这种两段式的集成学习模型能够更好地学习到材料数据的内在关系,并具有较好的预测效果和较强的泛化能力.

The band gap is an important parameter that can affect the physical and chemical properties of perovskite oxide composites,such as their conductivity and photoelectricity.To identify new perovskites for different applications,their band gap should be predicted via machine learning.Herein,a two-stage ensemble learning model that can predict the band gap of perovskite oxide composites is proposed by combining multiple individual base learners via a certain strategy.The first stage involves individual test functions produced by multiple regression learners.All individual base learners and some specific descriptors are aggregated into an ensemble model in the second stage.Subsequently,a dataset comprising the data of 210 ABX_(3)-type perovskites is used to evaluate the proposed ensemble learning model.Results show that the proposed two-stage ensemble methodology can improve the generalization performance.Its successful application on ABX_(3)-type perovskites indicates the effectiveness and practicability of ensemble learning in material research.