Atomic-Scale Rigidity of NTO Molecular Chains Under Perturbation Investigated Using Deep Learning

The mechanical sensitivity of energetic materials is closely linked to the stability of their microstructures; however, in situ observation of their dynamic response under external mechanical stimuli at the atomic scale remains challenging. Here, we propose a deep-learning-based intelligent analysis method for scanning tunneling microscopy (STM) images of a next-generation insensitive energetic material 3-nitro-1,2,4-triazol-5-one (NTO). We design SpecMol, a lightweight segmentation network with frequency-domain awareness, which achieves high-precision segmentation and orientation recognition of individual NTO molecules in adsorption images. Building upon this, we apply localized external forces to one-dimensional NTO nanochains via in situ STM tip manipulation and quantitatively analyze the geometric evolution of their fundamental building blocks—dimers. Experimental results reveal that, following mechanical perturbation, the relative orientation angle within the dimer (averaging approximately 14.55°) remains highly stable (CCC = 0.834), confirming the remarkable structural rigidity of NTO dimers. This study provides, for the first time, direct microscopic evidence at real-space atomic resolution for the low mechanical sensitivity of NTO, elucidating that its exceptional local structural stability originates from rigid dimeric units stabilized by an extensive hydrogen-bonding network. Our findings not only deepen the fundamental understanding of the safety performance of energetic materials but also demonstrate the powerful potential of integrating artificial intelligence with advanced characterization techniques for molecular-scale functional materials research.