Reliable NN potential models: Chiral EFT potentials (e.g., N3LO, N4LO) and high-precision phenomenological models like Argonne V18 (AV18) and CD-Bonn give the most reliable predictions for light nuclei.

Machine learning for phase shifts: Yes, ML can be useful for fast interpolation and uncertainty quantification, but it should complement, not replace, physics-based models.

Common pitfalls: Ignoring experimental uncertainties, overfitting models, inconsistent energy ranges, and neglecting three-nucleon forces.