Wang Yunlei, Lv Haifeng, Wu Xiaojun
State Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Sciences, Key Laboratory of Materials Sciences for Energy Conversion, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), CAS Center for Excellence in Nanoscience, University of Science and Technology of China Hefei Anhui 230026 China
Chem Sci. 2025 Mar 14;16(17):7320-7328. doi: 10.1039/d4sc08616c. eCollection 2025 Apr 30.
Metal-supported borophene exhibits significant polymorphism and an inherently complex potential energy landscape, posing challenges to exploring its structural diversity. In this study, we integrate a neural network-driven machine learning potential with stochastic surface walking global optimization and an active learning framework to comprehensively map the potential energy surface (PES) of large-size borophene on an Ag(100) substrate. Our exhaustive search identifies 59 857 local minima across 556 distinct supercells, revealing a PES segmented into multiple energy basins and three major funnels. Among the low-energy configurations, 1391 low-energy structures extend to the nanometre scale, showcasing a diverse array of mixed-phase borophene architectures, including monolayer ribbons (β and χ) and bilayer fragments (BL-α, BL-α, BL-α, BL-α, and BL-αα). Notably, the global minimum structures feature monolayers composed of alternating χ and β ribbons and bilayers formed from BL-α, BL-αα, and BL-α fragments. All mixed-phase borophenes exhibit metallic properties, and their simulated scanning tunneling microscopy (STM) images are provided to facilitate future experimental validation. These findings highlight the extraordinary structural complexity and rich polymorphism of borophene on extended metal surfaces, offering valuable insight into their formation, stability, and potential for tunable electronic properties.
金属支撑的硼烯表现出显著的多态性和固有的复杂势能面，这对探索其结构多样性构成了挑战。在本研究中，我们将神经网络驱动的机器学习势能与随机表面行走全局优化及主动学习框架相结合，以全面绘制Ag(100)衬底上大尺寸硼烯的势能面（PES）。我们的详尽搜索在556个不同的超胞中识别出59857个局部极小值，揭示了一个被分割成多个能量盆地和三个主要漏斗的势能面。在低能量构型中，1391个低能量结构延伸至纳米尺度，展示了一系列多样的混合相硼烯结构，包括单层带（β和χ）和双层片段（BL-α、BL-α、BL-α、BL-α和BL-αα）。值得注意的是，全局极小值结构的特征是由交替的χ和β带组成的单层以及由BL-α、BL-αα和BL-α片段形成的双层。所有混合相硼烯都表现出金属特性，并提供了它们的模拟扫描隧道显微镜（STM）图像，以方便未来的实验验证。这些发现突出了硼烯在扩展金属表面上非凡的结构复杂性和丰富的多态性，为其形成、稳定性及可调电子特性的潜力提供了有价值的见解。
