Belciu Miruna-Ioana, Velea Alin
National Institute of Materials Physics, Atomistilor 405A, 077125 Magurele, Romania.
Faculty of Physics, University of Bucharest, Atomistilor 405, 077125 Magurele, Romania.
Molecules. 2025 Apr 14;30(8):1745. doi: 10.3390/molecules30081745.
Chalcogenide glasses (ChGs) are a class of amorphous materials presenting remarkable mechanical, optical, and electrical properties, making them promising candidates for advanced photonic and optoelectronic applications. With the increasing integration of artificial intelligence in modern materials design, we are able to systematically select, prepare, and optimize appropriate compositions for desired applications in a manner that was unachievable before. This study employs various machine learning models to reliably predict the refractive index at 20 °C using a small dataset of 541 samples extracted from the SciGlass database. The input for the algorithms consists of a selected set of physico-chemical features computed for the chemical composition of each entry. Additionally, these algorithms served as inner models for an ensemble logistic regression estimator that achieved a superior R2 value of 0.8985. SHAP feature analysis of the second-best model, CatBoostRegressor (R2 = 0.8920), revealed the importance of elemental density, atomic weight, ground state atomic gap, and fraction of p valence electrons in tuning the value of the refractive index of a chalcogenide compound.
硫族化物玻璃（ChGs）是一类具有卓越机械、光学和电学性能的非晶态材料，这使其成为先进光子和光电子应用的理想候选材料。随着人工智能在现代材料设计中的日益融入，我们能够以前所未有的方式系统地选择、制备和优化适合特定应用的成分。本研究使用各种机器学习模型，利用从SciGlass数据库中提取的541个样本的小数据集，可靠地预测20°C时的折射率。算法的输入包括为每个条目的化学成分计算的一组选定的物理化学特征。此外，这些算法用作集成逻辑回归估计器的内部模型，该估计器实现了0.8985的卓越R2值。对次优模型CatBoostRegressor（R2 = 0.8920）的SHAP特征分析揭示了元素密度、原子量、基态原子间隙和p价电子分数在调节硫族化物化合物折射率值方面的重要性。
