An interface between Newton-X and the MLatom 92 (mlatom.com) allows simulating NEA spectra (see Section 4.4) driven by supervised machine learning predictions. The ML-NEA method uses transition energies and oscillator strengths computed with electronic structure methods for a minimum ensemble (typically, several hundreds of geometries) to train machine learning models to predict transition energies and oscillator strengths for the NEA spectrum simulation with statistically convergent ensembles (50k geometries or more) at minimum additional cost. The ML-NEA algorithm automatically determines the size of the minimum ensemble. The ML-NEA results are independent of the arbitrary line-width parameter  [eq (32)]. A tutorial for applying ML-NEA is available on the MLatom website.