B. Basha

Machine Learning-Driven Prediction of Optical Absorption in Composition-Dependent Truncated Pyramidal GaN/AlxGa1−xN Quantum Dots

Findings demonstrate that KNN is particularly effective for local interpolation within the sampled domain, while ANN provides stronger composition-wise generalization, which offers an efficient surrogate for computationally demanding numerical simulations of the optical properties of quantum nanostructures.