García, Jose H.Quiñones Andrade, Alba2025-09-182025-09-182025-06https://hdl.handle.net/2445/223242Treballs Finals de Grau de Física, Facultat de Física, Universitat de Barcelona, Curs: 2025, Tutor: Jose Hugo GarciaAccurate band gap prediction of two-dimensional materials holds significant scientific and technological value for the development of electronic and optoelectronic devices. In contrast to the high computational cost associated with traditional first-principles methods, machine learning offers a promising and cost-effective alternative for band gap prediction. In this work, we demonstrate that the combination of artificial neural networks and an active learning algorithm leads to a highly data-efficient method for predicting band gaps of 2D materials while maintaining accuracy, with L1-regularization analyzing feature selection. This approach achieves a computational cost reduction by shrinking the original dataset by 80% compared to traditional training approaches9 p.application/pdfengcc-by-nc-nd (c) Quiñones, 2025http://creativecommons.org/licenses/by-nc-nd/3.0/es/Ciència dels materialsFísica computacionalTreballs de fi de grauMaterials scienceComputational physicsBachelor's thesesinfo:eu-repo/semantics/bachelorThesisinfo:eu-repo/semantics/openAccess