In conclusion, we have computationally demonstrated the impact of the electronic and structural features on the activation energy required for N 2 gas release by considering 4 common backbones of diazo compounds and 14 functional groups. Based on our chemical intuition, we introduced a few attribute-based descriptors. The efficiency of this feature space was examined by training an ML model and explaining its interpretability. Our study resulted in several significant findings, which are detailed below. We conclude that (i) the nature of the functional group which is not directly bonded to the carbon bearing the diazo group has little influence on the activation barrier for N 2 release, (ii) to lower the activation energy, more positive and negative partial charges on C and N2 are needed, (iii) a more positive C charge decreases the π-donor ability of carbene lone pair to the π * of N 2 fragment, (iv) the more negative N2 charge shows a weak interaction between the N 2 lone pair and the carbene vacant p orbital, and (v) the chemical descriptors used for the ML model training led to a powerful predictability that confirms the efficiency of our feature space selection. Our research can contribute to a better understanding of the N 2 release transformation and gives experimental chemists crucial information about the mechanism of the reactions which are useful in designing new syntheses.