@inproceedings {744,
	title = {Predicting cubic and orthorhombic crystal band gaps via two-stage support-vector machine framework},
	booktitle = {Proceedings of the 44th Samahang Pisika ng Pilipinas Physics Conference},
	year = {2026},
	month = {17-20 Jun 2026},
	pages = {SPP-2026-PC-11},
	address = {Los Ba{\~n}os},
	abstract = {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. Structural and elemental descriptors were employed as features after rigorous pre-processing to eliminate data leakage. Our methodology implements a radial basis function kernel, first utilizing a support-vector classifier to distinguish metallic from non-metallic phases, followed by a support-vector regressor to predict specific gap values. Results demonstrate exceptional predictive performance, with the classification stage achieving near-perfect accuracy and the regression stage yielding R{\texttwosuperior} values exceeding 0.99 for both crystal systems. Specifically, the model achieved a testing mean absolute error of 0.0343 eV for cubic and 0.0420 eV for orthorhombic structures. These errors approach the room-temperature thermal energy scale (kT ≈ 0.0259 eV), confirming the model{\textquoteright}s ability to serve as a high-precision alternative. Overall, this pipeline provides a robust alternative to traditional DFT simulations, enabling efficient high-throughput material screening within the established generalized gradient approximation functionals baseline.},
	url = {https://proceedings.spp-online.org/article/view/SPP-2026-PC-11},
	author = {Angel Nicole E. Lubuguin and David D. Daffon and Gennevieve M. Macam}
}
