Thin film thickness of Polystyrene on the glass substrates

Advent in machine learning is leaving a deep impact on various sectors including the material science domain. The present paper highlights the application of various supervised machine learning regression algorithms such as polynomial regression, decision tree regression algorithm, random forest algorithm, support vector regression algorithm and artificial neural network algorithm to determine the thin film thickness of Polystyrene on the glass substrates. The results showed that polynomial regression machine-learning algorithm outperforms all other machine learning models by yielding the coefficient of determination of 0.96 approximately and mean square error of 0.04 respectively.机器学习的兴起对包括材料科学领域在内的诸多行业均产生了深远影响。本文重点阐述了多种监督式机器学习回归算法的应用，包括多项式回归（polynomial regression）、决策树回归算法、随机森林算法、支持向量回归算法以及人工神经网络算法，用于测定玻璃基板上聚苯乙烯（Polystyrene）薄膜的厚度。实验结果表明，多项式回归机器学习算法的性能优于其余所有机器学习模型，其分别取得了约0.96的决定系数与0.04的均方误差。