Young’s Modulus of Calcium-Alumino-Silicate Glasses: Insight From Machine Learning

Abstract

Modern technologies require the development of new materials with exceptional properties. Machine Learning (ML) and Deep Learning (DL) techniques have become important tools for discovering new materials and predicting the properties of specific materials, such as glasses. In this paper, we used ML and DL techniques to predict the Young's modulus E of Calcium-Alumino-Silicate (CAS) glasses based on their chemical composition. We evaluated four different algorithms, including Polynomial Regression (PR), Random Forest (RF), K-Nearest Neighbors (KNN), and Multi-Layer Perceptron Regressor (MLPRegressor). We found that the PR algorithm provides excellent predictions without Cross-Validation (CV), while the MLPRegressor yields the best performance when CV is implemented.