Mechanisms of strength, thermal stability, and doping effects in metal-doped bilayer borophene for energy storage applications using a DFT-trained deep-learning potential

Abstract

Bilayer borophene exhibits exceptional in-plane properties; however, its failure mechanisms and thermal robustness at realistic scales remain unclear, limiting its potential for applications in energy storage devices. We develop a deep-learning potential (DLP) trained on density-functional-theory (DFT) data (implemented with the DeepMD framework) and use it to investigate the coupled mechanical–thermal response of metal-doped bilayer borophene for energy storage applications. Focusing on two topologies (p6m2 and p6mmm) with substitutional Al and Ru, we find that p6m2 exhibits higher in-plane stiffness and strength than p6mmm, but reduced ductility due to earlier bond localization. Al doping increases stiffness while promoting brittle failure, whereas Ru preserves ductility and exerts an explicitly phase-dependent influence on thermal stability. Using the Lindemann-criterion analysis, we quantify the structural transition temperatures (STT) of pristine and doped systems as functions of phase, dopant, and concentration. The p6mmm phase exhibits a higher STT than p6m2, reflecting its more robust interlayer bonding network. Al consistently decreases the STT temperature, whereas Ru modulates it in a phase-dependent manner—substantially increasing the STT for p6m2 while only slightly reducing it for p6mmm, which nonetheless remains more stable than its Al-doped counterparts. These results provide atomic-level insight into the designability of metal-doped bilayer borophene and highlight the potential of DLP-based simulations for exploring complex 2D materials under realistic operating conditions.