论文标题
检测具有贝叶斯优化的Cu（111）上（1s）-Camphor的稳定吸附物
Detecting stable adsorbates of (1S)-camphor on Cu(111) with Bayesian optimization
论文作者
论文摘要
通过我们当前的研究工具，确定有机无机界面的原子结构具有挑战性。从显微镜图像解释复杂分子吸附物的结构可能很困难，并且使用原子模拟查找最稳定的结构仅限于由于高维相空间而引起的势能表面的部分探索。在这项研究中，我们将最近开发的贝叶斯优化结构搜索（BOSS）方法作为识别非平面吸附物结构的有效解决方案。我们将带有密度功能理论模拟的boss应用于Cu（111）表面上的（1s） - 脑中的稳定吸附物结构。我们确定了8种独特类型的稳定吸附物之间的最佳结构，其中通过氧气（全球最小值）或通过碳氢化合物到Cu（111）表面的樟脑化学物质或物理学。这项研究表明，新的跨学科工具（例如Boss）促进了复杂的表面结构及其特性的描述，并最终使我们能够调整高级材料的功能。
Identifying the atomic structure of organic-inorganic interfaces is challenging with our current research tools. Interpreting the structure of complex molecular adsorbates from microscopy images can be difficult, and using atomistic simulations to find the most stable structures is limited to partial exploration of the potential energy surface due to the high-dimensional phase space. In this study, we present the recently developed Bayesian Optimization Structure Search (BOSS) method as an efficient solution for identifying the structure of non-planar adsorbates. We apply BOSS with density-functional theory simulations to detect the stable adsorbate structures of (1S)-camphor on the Cu(111) surface. We identify the optimal structure among 8 unique types of stable adsorbates, in which camphor chemisorbs via oxygen (global minimum) or physisorbs via hydrocarbons to the Cu(111) surface. This study demonstrates that new cross-disciplinary tools, like BOSS, facilitate the description of complex surface structures and their properties, and ultimately allow us to tune the functionality of advanced materials.
