Predicting cubic and orthorhombic crystal band gaps via two-stage support-vector machine framework

Electronic band gap is a fundamental property determining the suitability of materials for modern electronic applications. While density functional theory (DFT) is the standard for calculating these values, low-cost functionals underestimate experimental band gaps by approximately 40%. This study aims to replicate these DFT-calculated results using machine learning with high fidelity. We developed a two-stage support-vector machine framework to predict the band gaps of cubic and orthorhombic crystal systems using a dataset from the Materials Project.