Xie Yuhao, Wang Xiangfu
College of Integrated Circuit Science and Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210023, China.
College of Electronic and Optical Engineering & College of Flexible Electronics (Future Technology), Nanjing University of Posts and Telecommunications, Nanjing 210023, China.
Nanomaterials (Basel). 2025 Jun 3;15(11):860. doi: 10.3390/nano15110860.
Based on the components of glasses, four algorithms, namely K-Nearest Neighbor, Random Forest, Support Vector Machine, and eXtreme Gradient Boosting, were used to construct an optimal machine learning model to predict the thermal and optical properties of oxyfluoride glass, namely glass transition temperature, density, Abbe number, liquidus temperature, thermal expansion coefficient, and refractive index. We perform SHAP analysis on the constructed machine learning model to explain the effects of different components on the properties. Based on the trained machine learning models, we developed several ternary system prediction maps that can effectively predict the properties of glasses composed of different proportions of components. This study provides a method to design new oxyfluoride glasses only knowing the components of glasses, which is instructive for the development of new types of oxyfluoride glasses as well as for computer-aided reverse design.
基于玻璃的成分，使用了四种算法，即K近邻算法、随机森林算法、支持向量机算法和极端梯度提升算法，构建了一个最优的机器学习模型，以预测氟氧化物玻璃的热学和光学性能，即玻璃化转变温度、密度、阿贝数、液相线温度、热膨胀系数和折射率。我们对构建的机器学习模型进行SHAP分析，以解释不同成分对性能的影响。基于训练好的机器学习模型，我们绘制了几个三元系统预测图，这些图可以有效地预测由不同比例成分组成的玻璃的性能。本研究提供了一种仅通过了解玻璃成分来设计新型氟氧化物玻璃的方法，这对新型氟氧化物玻璃的开发以及计算机辅助逆向设计具有指导意义。
