NEA simulates steady-state and time-resolved spectra by performing an incoherent sum over independent transitions from different nuclear configurations . These configurations are sampled to represent the nuclear wave function distribution in the source state (the lowest triplet state for phosphorescence). Standard NEA, however, does not include any information about the target state wave function (the ground state for emission spectra) . Therefore, the band shape is entirely determined by the source-state features, and vibrational structures are neglected. Moreover, NEA has a high intrinsic cost of a few hundred single-point calculations, which can be alleviated using machine learning . This paper implements a strategy for simulating steady-state phosphorescence spectra with NEA.