TL;DR
A hybrid approach combining the finite difference method to solve the Schrödinger equation and artificial intelligence models was used to predict the electronic properties of the quantum dot, offering a faster and more efficient alternative for modeling QDs.
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.
This work analyzes the effect of hydrostatic pressure on the predicting excitonic optical response of a 2D Ultra-thin GaAs-system. The data are generated from a numerical model incorporating quantum confinement and excitonic effects, capturing the system's response to incident photon energy and applied pressure. To red...
This work focuses on the modeling and prediction of the optical absorption coefficient in GaAs 2D nanostructure subjected to hydrostatic pressure. The database is generated from numerical calculations describing the optical absorption of exciton confined in GaAs 2D nanostructure. The obtained data are exploited to deve...
The results demonstrate that machine learning can efficiently identify promising dopant configurations while providing reliable predictions of dopant-dependent material properties, and offers a practical approach for the computational design of doped 2D oxide semiconductors for future optoelectronic and sensing applica...
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