硅酸盐学报2025，Vol.53Issue(10)：2766-2776,11.DOI:10.14062/j.issn.0454-5648.20250278
改进的Wasserstein生成式对抗网络用于氧化物玻璃电学性能的数据增强建模
Modeling Electrical Properties of Oxide Glass via Improved Wasserstein Generative Adversarial Network for Data Augmentation
摘要
Abstract
Introduction With the advancement of the Materials Genome Initiative and the establishment and improvement of the glass database,data-driven machine-learning methods penetrate the field of glass materials,and remarkable progress is made in the modeling of the physical,mechanical,and optical properties of glass materials.In recent years,oxide glasses have found increasingly widespread applications in the field of electronic applications,and their electrical properties as critical indicators have attracted much attention.However,modeling on the electrical properties of oxide glasses remains a challenge,and insufficient data suppress the model's performance.To address the issue of limited data availability on the electrical properties of oxide glasses,this work was to propose a data augmentation framework named WGAN-GP-CP tailored for glass materials. Methods Based on the Wasserstein generative adversarial network with gradient penalty(WGAN-GP)model,an additional composition penalty term(CP)was added to the generator's loss function,and a method for evaluating sample quality was proposed,which could assess the quality of the generated samples from three aspects,i.e.,diversity,accuracy,and uniqueness.Moreover,the XGBoost algorithm was employed for model training,and the model was interpreted through SHAP single-feature analysis and feature interaction analysis. Results and discussion The trained generators can generate high-quality synthetic samples,expanding the existing datasets effectively.The results of comparative studies demonstrate that the data-augmented models exhibit mitigated overfitting and significantly enhanced generalization capability.The generalization performances of the room-temperature bulk resistivity,relative dielectric constant,and dielectric loss models increase from 0.784 to 0.838,0.862 to 0.897,and 0.801 to 0.861,respectively.The results obtained from SHAP analysis are generally consistent with the classical physicochemical understanding of the electrical properties of oxide glasses.In addition,some interesting mixing effects and interactive relationships are also revealed. Conclusions The data augmentation framework proposed in this work offered a strategy for developing high-performance data-driven models under data scarcity conditions.The insights from SHAP single-feature analysis and feature interaction analysis could provide an important guidance for a deeper understanding of the relationship between the composition and electrical properties of oxide glasses.This research shifted the optimization of oxide glass electrical properties from empirically driven trial-and-error methods to data-driven scientific design frameworks,laying a foundation for the development of high-performance glass materials in the field of electronic applications.关键词
氧化物玻璃/电学性能/机器学习/数据增强/SHAP分析Key words
oxide glasses/electrical properties/machine learning/data augmentation/SHAP analysis分类
化学化工引用本文复制引用
田静,李苑,官敏,郑际杰,储静远,刘涌,韩高荣..改进的Wasserstein生成式对抗网络用于氧化物玻璃电学性能的数据增强建模[J].硅酸盐学报,2025,53(10):2766-2776,11.基金项目
国家"十四五"重点研发计划(2022YFB3603300) （2022YFB3603300）
上海市2022年度科技创新行动计划国际科技合作项目(22520730500). （22520730500）
