Tuning mechanism and machine learning prediction of second harmonic generation in modified Tietz quantum wells

We develop a symmetry-tunable framework for second-harmonic generation (SHG) in modified Tietz quantum wells and a fast, accurate surrogate to predict SHG under external fields. Within an effective-mass/envelope model, we quantify how structural parameters and static fields reshape level intervals and intersubband dipole couplings, thereby controlling SHG amplitude and lineshape. A magnetic field compresses the envelope and tends to equalize subband spacings, damping asymmetry-driven SHG at high fields, whereas an electric field tilts the well, breaks inversion symmetry, and can realign resonances; the trends are governed by the detuning between one- and two-photon channels and by the strength of the dipole overlap chain. Building on these insights, we train a shallow feed-forward network that maps the electric and magnetic fields directly to the SHG peak, achieving near-unity R² with very low RMSE on both medium- and small-sample datasets, and retaining accuracy over modest extrapolation, enabling rapid parameter selection without repeated eigenproblem and susceptibility evaluations. The parameter ranges and model knobs are qualitatively linked to GaAs/AlGaAs controls, providing a practical route from symmetry tuning to SHG prediction in experimentally relevant regimes.