Identifying the determining factors of detonation properties for linear nitroaliphatics with high-throughput computation and machine learning

Abstract

In this work, a high-throughput computation (HTC) and machine learning (ML) combined method was applied to identify the determining factors of the detonation velocity ($v_d$) and detonation pressure ($p_d$) of energetic molecules and screen potential high-energy molecules with acceptable stability in a high-throughput way. The HTC was performed based on 1725 sample molecules abstracted from a dataset of over 10⁶ linear nitroaliphatics with 1- to 6-membered C backbones and three types of substituents, namely single nitro group (-NO₂), nitroamine (-NNO₂), and nitrate ester (-ONO₂). ML models were established based on the HTC results to screen high-energy molecules and to identify the determining factors of $v_d$ and $p_d$. Compared with quantum chemistry calculation results, the absolute relative errors of $v_d$ and $p_d$ obtained using the ML models were less than 3.63% and 5%, respectively. Furthermore, eight molecules with high energy and acceptable stability were selected as potential candidates. This study shows the high efficiency of the combination of HTC and ML in high-throughput screening.