Performance Prediction of Metal Nitride Energetic Materials Based on Structural Searching and Machine Learning

Abstract

Metal nitrides exhibit broad prospects as high-energy-density materials (HEDMs) due to their exceptional energy release characteristics and environmentally friendly decomposition products. However, a fundamental challenge for this class of HEDMs lies in the trade-off between high energy density and high stability. Here, we employed a crystal structure search method to obtain numerous metal nitride configurations with different elements and stoichiometries and characterized their properties using first-principles calculations and ab initio molecular dynamics (AIMD) simulations. Based on the dataset, we developed descriptors related to the elemental and structural properties and trained regression models to predict the energy density of the metal nitrides. Additionally, multiple classifier models were trained to assess their stability. Through these machine learning models, we analyzed the features affecting the energy density and stability of metal nitrides and identified three key descriptors: the average distance between each nitrogen atom and its nearest neighbor (DNN), the stoichiometric ratio (N:M), and the average number of N-N bonds per nitrogen atom (aveNNBonds). Based on these insights, we propose a design principle for advanced metal nitride HEDMs: prioritizing high nitrogen-to-metal ratios, light metal elements, and structures wherein nitrogen atoms are spatially separated by the metal matrix, which minimizes N-N bonds and favors dominant M-N bonding. Moreover, our analysis revealed a class of metal nitrides with high nitrogen content that combines considerable stability and high energy density, demonstrating the power of machine learning in material HEDM design.