Benchmarking Machine Learning Models on a Dielectric Constant Database for Bandgap Prediction

In this study, we investigate the performance of several regression models by utilizing a database of dielectric constants.First, the database is processed using the Matminer Python library to create features, and then divided into training, validation, and testing subsets.We evaluate several models: Linear Regression, Random Forest, Gradient Boosting, XGBoost, Support Vector Regression, and Feedforward Neural Network, with the objective of predicting the bandgap values.The results indicate superior performance of tree-based ensemble models over Linear Regression and Support Vector Regression.Additionally, a Feedforward Neural Network with two hidden layers demonstrates comparable proficiency in capturing the relationship between the features generated by Matminer and the bandgap target values.