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自发极化强度是衡量铁电材料极化能力的关键指标。新兴的纤锌矿铁电材料因较高的自发极化而受到广泛关注，但目前对影响这一性质的关键因素的理解仍然不足。本文通过结合机器学习和第一性原理方法来解决这一问题。首先，计算了40种二元和89种简单三元纤锌矿材料的自发极化强度，并从元素基本属性、晶体结构参数和电子性质中提取了多种特征。随后，采用Boruta算法和距离相关系数分析方法进行特征筛选，提出了一个全面而精确的纤锌矿材料自发极化强度的机器学习预测模型。更重要的是，借助SHapley Additive exPlanations分析方法，阐明了影响自发极化强度的关键因素是阳离子离子势的均值IPi_Aave和晶胞参数a等。这种多因素的影响机制弥补了目前对自发极化强度影响因素理解的缺乏，为系统评估新兴纤锌矿材料的自发极化强度做出了基础性贡献，有助于加快性能优异的纤锌矿铁电材料的筛选。本文数据集可在科学数据银行https://www.doi.org/10.57760/sciencedb.j00213.00073中访问获取(审稿阶段请通过私有访问链接查看本文数据集https://www.scidb.cn/s/mAVvym)。Emerging wurtzite ferroelectric materials have attracted significant interest due to their high Spontaneous polarization magnitude (Ps). However, there is a limited understanding of the key factors that influence Ps. Herein, machine-learning regression models were developed to predict the Ps using datasets comprising 40 binary and 89 simple ternary wurtzite materials. Features were extracted based on elemental properties, crystal parameters and electronic properties. Feature selection was carried out using the Boruta algorithm and distance correlation analysis, resulting in a comprehensive machine learning model. Furthermore, SHapley Additive exPlanations analysis identified the average cation-ion potential (IPi_Aave) and the lattice parameter (a) as significant determinants of Ps, with IPi_Aave having the most prominent effect. A lower IPi_Aave corresponds to a lower Ps in the materials. Additionally, a exhibit an approximately negative correlation with Ps.
This multifactorial analysis fills the existing void in understanding the determinants of Ps, making a foundational contribution to the evaluation of emerging wurtzite materials and expediting the discovery of high-performance ferroelectric materials.
The dataset in this article can be accessed on the Scientific Data Bank https://www.doi.org/10.57760/sciencedb.j00213.00073. Please access the dataset of this article through a private link during the review stage https://www.scidb.cn/s/mAVvym).-
Keywords:
- Wurtzite ferroelectric materials /
- Spontaneous polarization magnitude /
- Machine learning /
- first principles methods
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