Discovery of high energy and stable prismane derivatives by the high-throughput computation and machine learning combined strategy

Abstract

Motivated by the excellent detonation performance of octanitrocubane, prismane is another potential backbone with high strain energy in energetic molecule design. In this work, we aim to screen out candidates of highly energetic molecules from the space of prismane derivatives. The high-throughput computation (HTC) is performed based on 200 molecules derived from the molecule space of 1503 prismane derivatives with four substituents. Based on the calculated results, the machine learning (ML) models of density, detonation velocity, detonation pressure, heat of formation and detonation heat are established, and thereby the performances of the remaining 1303 samples are predicted. It is found that the –NHNO2 group increases density, while both –NO2 and –C(NO2)3 groups promote detonation performances. Based on the detonation velocity and bond dissociation energy as criteria representing energy and molecular stability, four molecules were screened out with good detonation performance and acceptable thermal stability. This work demonstrates the efficiency of HTC and ML combined strategy for screening high-quality energetic molecules.

Keywords

• Prismane derivatives
• Energetic molecular design
• High-throughput computation
• Machine learning