Methods like machine learning (ML) or quantum chemistry (QC) calculations are employed for this purpose . For example, the Neural electron-ionization mass spectrometry (NEIMS) software uses a multi-layer perceptron (MLP) architecture to generate low-resolution EI mass spectra from molecular fingerprints . The specific model presented in the paper was trained in a supervised manner on the NIST/EPA/NIH Mass Spectral Library 2017 database. State of the art ML methods, such as graph neural networks (GNNs) and transformers, have demonstrated superior performance compared to MLP based models and have been applied in various ways for mass spectra prediction tasks . In particular, Zhu and Jonas 10 utilize a representation learning approach to predict the likelihood of subformulae within a predetermined depth of bond breakages. They then leverage these probabilities to scale the ion intensities in substructure spectra derived from isotopic patterns. Their study successfully predicted EI high-resolution mass spectra, including exact peaks, for molecules from PubChem, using an artificial high-resolution dataset. However, despite these advancements, ML approaches typically require substantial amounts of high-quality training data. Consequently, the broader applicability of ML methods is hindered by the scarcity of available EI high-resolution mass spectral libraries, primarily limiting their usage to the prediction of low-resolution spectra.