The above-simplified description prompted us to conduct a DFT-based investigation, examining a database of diazo compounds consisting of 4 common backbones decorated with 14 functional groups (Scheme 2). Such a database allows us to discover leading features and optimize them in order to design an efficient diazo compound for N 2 release. To this end, we also used a machine learning (ML) approach to analyze DFT results and identify relevant chemical descriptors. The accuracy of ML has now reached a level comparable to that of DFT . It is important to note that DFT is an accurate approximation, but scales with system size, whereas ML is fast but limited by database size and reliability. By combining these two approaches, we can uncover previously unseen correlations and gain a deeper understanding of chemical reactions.