@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}
}
@inproceedings {681,
	title = {Prediction of orthorhombic lattice constants using machine learning},
	booktitle = {Proceedings of the 42nd Samahang Pisika ng Pilipinas Physics Conference},
	year = {2024},
	month = {3{\textendash}6 Jul 2024},
	pages = {SPP-2024-PB-22},
	address = {Batangas City},
	abstract = {A crystal structure is composed of a unit cell repeating itself to occupy a space, forming what is known as a lattice. This arrangement is dictated by the structure{\textquoteright}s lattice constants. Lattice constants are integral for investigation into the properties of crystal materials. However, current methods to determine such constants may be computationally exhaustive and time consuming. In this study, we utilized a random forest machine learning model to predict the lattice constants of orthorhombic crystal structures. This model was trained using the various materials{\textquoteright} structural properties. To quantitatively evaluate the quality of our our model, we compared the model generated lattice constants with the experimental values, and obtained the following coefficients of determination (R{\texttwosuperior}): 0.860, 0.825, and 0.826 for the a, b, and c constants respectively, which we found to be similar with previous studies. Moreover, we found the resultant mean squared error and mean absolute error for each lattice constant to be minimal, further supporting the overall performance of our model. Furthermore, to illustrate the weight of each property on the training of the model, we calculated the feature importance across the three random forest regressors. We found the key features to be unit cell volume, crystal system type, mean atomic number, and total atomic number.},
	url = {https://proceedings.spp-online.org/article/view/SPP-2024-PB-22},
	author = {David D. Daffon and Adrianna Victoria Beatrice J. Pantoja and Gennevieve M. Macam}
}
