In recent years, the combination of abundant data from computational and experimental methods, along with advanced machine learning (ML) techniques, has propelled materials science into a new era, often called the fourth paradigm of scientific exploration. ML algorithms are now widely used to predict the mechanical and electronic properties of materials with greater efficiency and accuracy. Zhang et al. proposed a novel approach using multilevel descriptors for the ML-assisted screening of inorganic NLO crystals. ML models have been developed to predict the symmetry type of crystals and discover new noncentrosymmetric materials. To our knowledge, there exists only a single ML model based on few data points to predict NLO coefficient. This limitation arises due to the absence of an existing database specific to this property.